merge: 4b flag profile_level lite/standard/full en render_automatic_eda (lite 4.5s vs full 39.3s, verificado met)

Añade el parámetro profile_level a render_automatic_eda como preset de
consumo CPU/LLM que mapea a los flags existentes (run_models, run_series,
run_llm, sample). Tres niveles:

- lite (bajo consumo): run_llm=False, run_series=False, sample=2000 y modelos
  limitados a PCA + normalidad, SIN KMeans ni IsolationForest (lo caro en CPU).
  Para un vistazo rápido y barato.
- standard (default): comportamiento histórico — modelos completos, serie,
  sin LLM.
- full: standard + narrativa LLM por capítulo.

Precedencia: un flag explícito del caller (run_llm=..., run_models=..., etc.)
siempre prima sobre el default que fija el preset; el preset solo aplica al
parámetro que se deja en None.

Cableado del modo lite sin tocar profile_table (lo tocan otros agentes en
paralelo): profile_table NO corre los modelos (evita pagar KMeans +
IsolationForest); este pipeline los corre con run_eda_models(run_kmeans=False,
run_isolation=False) reusando ctx['raw_numeric'], y quita raw_numeric del ctx
para que el capítulo modelos no reproyecte clusters KMeans en vivo
(project_clusters_2d). geo_points ya queda derivado, así que geospatial no se
afecta.

Cambio aditivo y retro-compatible: sin profile_level el comportamiento es
idéntico al de v1.0.0 (standard). Tests nuevos cubren lite/standard, la
precedencia flag-sobre-preset, y la equivalencia del default con el histórico.
Bump 1.0.0 -> 1.1.0 + growth log en el .md. Skill /eda documenta --lite/--full.

Verificación: golden lite/standard/full sobre titanic — lite 4.8s (PCA+norm,
sin KMeans/iso/LLM/serie), standard 7.8s (modelos completos), full 38.3s
(+LLM). Suite render_automatic_eda + automatic_eda: 96 passed. fn index sin
error.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

.claude/commands/eda.md

@@ -27,6 +27,7 @@ Página madre del grupo: `docs/capabilities/eda.md` (léela primero para cargar
   - `--series` → `run_series=True` (estacionariedad ADF+KPSS, ACF/PACF, STL, retornos por columna numérica).
   - `--pdf` → `emit_pdf=True` (PDF A5 legacy de `render_eda_pdf`, legible en móvil).
   - `--legacy-only` → emite SOLO el PDF legacy (sin AutomaticEDA), para casos en que solo se quiera el PDF rápido.
+  - `--lite` / `--bajo-consumo` → `render_automatic_eda(profile_level="lite")`: EDA barato y rápido (CI, vistazo previo, máquina sin GPU/red). Apaga LLM y serie temporal y limita los modelos a **PCA + normalidad** (sin KMeans ni IsolationForest, lo caro en CPU), con `sample` reducido. `--full` → `profile_level="full"` (standard + narrativa LLM). Por defecto `profile_level="standard"` (comportamiento histórico). Un flag explícito (`--llm`, `--models`, ...) prima sobre el preset.
 
 Por defecto, **un EDA completo emite SIEMPRE el informe AutomaticEDA en sus dos formatos: PDF (A5 móvil) Y PPTX (16:9 para compartir)** con los 11 capítulos poblados (portada, overview, distribuciones, calidad, correlaciones, modelos, series, geoespacial, agregación, interpretación LLM). Usa el pipeline `render_automatic_eda` (o `profile_table(emit_automatic=True)`), que activa `run_models` y `run_series` para que los capítulos de modelos/series/geoespacial/agregación salgan poblados. Deja `run_llm` para cuando el usuario lo pida o interese la interpretación semántica + narrativa por capítulo (es la única parte que gasta tokens del modelo).
 
@@ -50,7 +51,8 @@ from pipelines.render_automatic_eda import render_automatic_eda
 # tablas de agregación). run_llm=True añade la narrativa LLM por capítulo.
 r = render_automatic_eda(
     "/ruta/datos.duckdb", "ventas",
-    run_models=True, run_series=True, run_llm=False, out_dir="reports",
+    profile_level="standard",  # "lite" = bajo consumo CPU/LLM; "full" = + narrativa LLM
+    out_dir="reports",
 )
 print("status:", r["status"])
 print("pdf:   ", r["pdf_path"], "(", r["n_pages"], "págs )")

.claude/commands/fleet.md

@@ -1,6 +1,6 @@
 ---
-description: Muestra la flota de Claudes vivos (sessionId + objetivo + estado) y, con argumento, salta con foco a esa conversación dentro de la sesión tmux fleet.
+description: Muestra la flota de Claudes vivos (sessionId + objetivo + estado) y, con argumento, salta con foco a esa conversación dentro de la sesión tmux fleet. `/fleet show` trae la TUI al contexto tmux actual.
-argument-hint: "[texto|sessionId|PID para saltar — vacío = listar la flota]"
+argument-hint: "[show | texto|sessionId|PID para saltar — vacío = listar la flota]"
 ---
 
 # /fleet — ver y navegar la flota de Claudes
@@ -33,9 +33,32 @@ cd "${FN_REGISTRY_ROOT:-$HOME/fn_registry}/apps/fleetview" && go build -o fleetv
    - la sesión actual / orquestador si la puedes identificar (su `session_id` coincide con el de quien invoca).
 4. Si la lista está vacía, indícalo y sugiere que el perfil fleet podría no estar activo (revisar `$FLEET_SOCKET` y que la sesión tmux exista).
 
+### `show` → traer la TUI al contexto tmux actual
+
+Si `$ARGUMENTS` es exactamente `show` (alias `open`/`attach`), el usuario quiere
+volver a ver el panel FleetView en el contexto/pane actual sin abrir ninguna
+ventana ni arrancar una flota nueva. Ejecuta:
+
+```bash
+"${FN_REGISTRY_ROOT:-$HOME/fn_registry}/apps/fleetview/fleetview" show
+```
+
+Comportamiento (decidido por la app, no abre terminal externa):
+
+- **dentro de tmux con la flota viva** → `select-window` de la window `console`
+  del socket fleet (trae la TUI al frente; no abre nada).
+- **fuera de tmux** → `attach` a la sesión fleet en la terminal actual (la reutiliza).
+- **sin flota viva** → error claro, exit 1, no abre nada (sugiere arrancarla con
+  `fleetclaude`).
+
+Es el equivalente del comportamiento de `fleetclaude` sin args invocado dentro de
+una flota viva (reuse de contexto): úsalo cuando ya tengas una flota corriendo y
+solo quieras recuperar la vista del panel. Para abrir una flota NUEVA aparte, usa
+`fleetclaude --new` (no este comando).
+
 ### Con argumentos → saltar con foco
 
-El usuario quiere que la interfaz tmux salte a una conversación concreta. `$ARGUMENTS` es el query: texto del objetivo, prefijo de `sessionId`, o PID.
+El usuario quiere que la interfaz tmux salte a una conversación concreta. `$ARGUMENTS` es el query: texto del objetivo, prefijo de `sessionId`, o PID (cualquier valor que no sea `show`).
 
 1. Ejecuta:
    ```bash

bash/functions/infra/launch_fleetclaude.md

@@ -3,10 +3,10 @@ name: launch_fleetclaude
 kind: function
 lang: bash
 domain: infra
-version: "1.6.0"
+version: "1.7.0"
 purity: impure
-signature: "launch_fleetclaude [--cwd <dir>] [--bin <path>] [--session <name>] [--reuse] [--cols <n>]"
+signature: "launch_fleetclaude [--cwd <dir>] [--bin <path>] [--session <name>] [--reuse] [--new] [--cols <n>]"
-description: "Entrypoint de FleetView: abre una ventana de terminal con una sesion tmux (socket aislado por perfil) de dos panes (TUI fleetview a la izquierda, claude --dangerously-skip-permissions a la derecha) para centralizar la flota de Claudes. La terminal se AUTO-DETECTA sin config por PC: kitty si esta instalado y hay display ($DISPLAY/$WAYLAND_DISPLAY), si no Windows Terminal (wt.exe) en WSL adjuntando via wsl.exe. El pane de la TUI corre dentro del bucle supervisor supervise_fleetview_tui, que la relanza si muere (crash/panic/kill), asi el panel de control NUNCA se pierde. Soporta PERFILES multiples: sin --session/--reuse cada invocacion abre un perfil nuevo (fleet, fleet2, fleet3, ...) con su propia flota; inyecta FLEET_SOCKET/FLEET_SESSION a la TUI para que cada panel vea solo sus Claudes. Instala atajos alt+flechas/alt+enter/alt+n que controlan la TUI desde cualquier pane, y fija el ancho del sidebar con hooks."
+description: "Entrypoint de FleetView: abre una ventana de terminal con una sesion tmux (socket aislado por perfil) de dos panes (TUI fleetview a la izquierda, claude --dangerously-skip-permissions a la derecha) para centralizar la flota de Claudes. REUSO DE CONTEXTO: si se invoca DENTRO de una flota tmux viva (su window 'console') sin --new, NO abre ventana ni crea un perfil nuevo; trae la TUI al pane/contexto actual (equivale a 'fleetview show'). El flag --new fuerza una flota+ventana nueva aunque estes en tmux. La terminal se AUTO-DETECTA sin config por PC: kitty si esta instalado y hay display ($DISPLAY/$WAYLAND_DISPLAY), si no Windows Terminal (wt.exe) en WSL adjuntando via wsl.exe. El pane de la TUI corre dentro del bucle supervisor supervise_fleetview_tui, que la relanza si muere (crash/panic/kill), asi el panel de control NUNCA se pierde. Soporta PERFILES multiples: fuera de tmux, o con --new, cada invocacion abre un perfil nuevo (fleet, fleet2, fleet3, ...) con su propia flota; inyecta FLEET_SOCKET/FLEET_SESSION a la TUI para que cada panel vea solo sus Claudes. Instala atajos alt+flechas/alt+enter/alt+n que controlan la TUI desde cualquier pane, y fija el ancho del sidebar con hooks."
 tags: [claude-fleet, infra, kitty, tmux, claude, fleetview, launcher, wsl, windows-terminal]
 params:
   - name: --cwd
@@ -14,12 +14,14 @@ params:
   - name: --bin
     desc: "Ruta al binario de la TUI fleetview que corre en el pane izquierdo. Opcional. Default: <repo>/apps/fleetview/fleetview. Si no es ejecutable, el pane izquierdo muestra un mensaje de como compilarla y deja una shell viva."
   - name: --session
-    desc: "Fija el perfil (socket+sesion tmux comparten nombre) por nombre exacto; reutiliza el existente si ya vive (idempotente sobre ese nombre). Opcional. Sin esta opcion, el perfil se elige automaticamente (primer nombre libre de la secuencia fleet, fleet2, ...)."
+    desc: "Fija el perfil (socket+sesion tmux comparten nombre) por nombre exacto; reutiliza el existente si ya vive (idempotente sobre ese nombre). Opcional. Sin esta opcion, el perfil se elige automaticamente (primer nombre libre de la secuencia fleet, fleet2, ...). Invocado DENTRO de tmux con un nombre DISTINTO al de la flota actual equivale a --new (pides otra flota: ventana nueva, sin reuse de contexto)."
   - name: --reuse
     desc: "Reattach al perfil principal 'fleet' en vez de abrir uno nuevo. Opcional. Recupera el comportamiento idempotente clasico (volver a invocar NO duplica la flota, reusa la existente)."
+  - name: --new
+    desc: "Fuerza una flota NUEVA en una ventana NUEVA (kitty/wt.exe) incluso estando dentro de una flota tmux. Opcional. Es la via explicita para abrir una FleetView aparte; sin este flag, invocado dentro de una flota viva se reusa el contexto actual (no abre ventana ni crea perfil)."
   - name: --cols
     desc: "Ancho en columnas del pane izquierdo (la TUI). Opcional. Default: 40."
-output: "Crea/reutiliza una sesion tmux detached con dos panes y lanza una ventana de terminal 'FleetView' adjunta a ella (kitty o Windows Terminal segun auto-deteccion), desacoplada del shell padre. Imprime el estado por stdout. Sin valor de retorno; exit 0 en exito."
+output: "Caso reuse de contexto (dentro de una flota tmux viva, sin --new): trae la TUI al pane/contexto actual con select-window de la window 'console' (o 'fleetview show' si el binario existe) y retorna 0, sin abrir nada. Caso ventana-nueva (fuera de tmux, o con --new): crea/reutiliza una sesion tmux detached con dos panes y lanza una ventana de terminal 'FleetView' adjunta (kitty o Windows Terminal segun auto-deteccion), desacoplada del shell padre. Imprime el estado por stdout. Sin valor de retorno; exit 0 en exito, !=0 con mensaje claro si no hay terminal ni contexto que reusar."
 uses_functions:
   - supervise_fleetview_tui_bash_infra
 uses_types: []
@@ -36,32 +38,44 @@ file_path: "bash/functions/infra/launch_fleetclaude.sh"
 ## Ejemplo
 
 ```bash
-# Via fn run (resuelve por nombre o ID):
+# DENTRO de una flota tmux viva (p. ej. en el pane del orquestador): reusa el
-fn run launch_fleetclaude
+# contexto, trae la TUI al pane actual. NO abre ventana ni crea perfil nuevo.
+fleetclaude
 
-# Perfil nuevo automatico (fleet la 1a vez; fleet2, fleet3, ... si ya hay uno):
+# FUERA de tmux: perfil nuevo automatico (fleet la 1a vez; fleet2, ... si ya hay
-launch_fleetclaude
+# uno) en una ventana de terminal nueva, reutilizando la terminal actual (attach):
+fleetclaude
+
+# Forzar una flota+ventana NUEVA aunque estes dentro de una flota tmux:
+fleetclaude --new
 
 # Reattach a la flota principal 'fleet' (comportamiento idempotente clasico):
-launch_fleetclaude --reuse
+fleetclaude --reuse
 
 # Perfil con nombre fijo y ancho de pane personalizado:
-launch_fleetclaude --session trabajo --cols 50
+fleetclaude --session trabajo --cols 50
+
+# Via fn run (resuelve por nombre o ID):
+fn run launch_fleetclaude
 ```
 
-Tras invocarlo aparece una ventana de terminal titulada `FleetView (<perfil>)` con dos
+Dentro de una flota viva, `fleetclaude` sin args reusa el contexto (la window
-panes lado a lado: a la izquierda la TUI `fleetview`, a la derecha una sesion de
+`console` pasa al frente). Fuera de tmux (o con `--new`) aparece una ventana de
-`claude --dangerously-skip-permissions`. Cada perfil es un socket+sesion tmux
+terminal titulada `FleetView (<perfil>)` con dos panes lado a lado: a la izquierda
-aislados con su propia flota: puedes tener varias FleetView abiertas a la vez.
+la TUI `fleetview`, a la derecha una sesion de `claude --dangerously-skip-permissions`.
-Por defecto, volver a invocarlo abre un perfil NUEVO (no reusa); usa `--reuse`
+Cada perfil es un socket+sesion tmux aislados con su propia flota: puedes tener
-o `--session <nombre>` para volver a una flota concreta.
+varias FleetView abiertas a la vez con `--new`.
 
 ## Cuando usarla
 
 Usala cuando quieras un unico punto de entrada a la flota de Claudes en vez de
 N ventanas kitty sueltas: lanzas `fleetclaude` y tienes la TUI de control y un
 Claude listo para trabajar en la misma ventana. Tipico al empezar la jornada o
-al retomar el trabajo en el repo `fn_registry`.
+al retomar el trabajo en el repo `fn_registry`. Si **ya estas dentro de una
+flota** (en el pane del orquestador) y solo quieres volver a ver la TUI, lanza
+`fleetclaude` sin args: trae el panel al contexto actual sin abrir otra ventana
+ni arrancar una flota duplicada. Usa `--new` solo cuando quieras DELIBERADAMENTE
+una segunda flota aparte.
 
 ## Gotchas
 
@@ -87,10 +101,27 @@ al retomar el trabajo en el repo `fn_registry`.
   funciona en un PC con kitty y en otro WSL sin kitty, cada uno elige su
   terminal. Causa raiz del sintoma "se lanza la flota pero no se ve": kitty no
   instalado en WSL hacia que la sesion tmux se creara sin ventana que la mostrara.
-- **Dentro de tmux abre ventana nueva**: si invocas `fleetclaude` desde dentro de
+- **Dentro de una flota tmux viva: reuse de contexto (no ventana nueva)**: si
-  una sesion tmux (`$TMUX` definido), NO hace `attach` anidado (rompe / avisa de
+  invocas `fleetclaude` sin `--new` desde dentro de una flota fleetview viva
-  nesting); cae a la ruta ventana-nueva (auto-deteccion de terminal). Fuera de
+  (`$TMUX` definido y el socket actual tiene una sesion homonima con window
-  tmux y con TTY, reutiliza la terminal actual con `exec tmux attach`.
+  `console`), NO abre ventana ni crea un perfil `fleetN+1`: trae la TUI al pane
+  actual (`fleetview show`, o `tmux -L <perfil> select-window -t <perfil>:console`
+  si el binario no esta compilado) y retorna 0. El perfil de la flota actual se
+  deriva de `$TMUX` (basename del socket = nombre `-L`), senal fiable aunque
+  `$FLEET_SOCKET` venga vacio (ver `detect_fleet_context`). **`--new`** fuerza el
+  comportamiento clasico (flota+ventana nueva); pasar `--session <otro>` distinto
+  al perfil actual equivale a `--new` implicito. Fuera de tmux y con TTY, reutiliza
+  la terminal actual con `exec tmux attach` (nunca `attach` anidado dentro de
+  tmux). Sin TTY ni contexto que reusar (atajo de escritorio/cron) cae a la ruta
+  ventana-nueva. Antes de este fix (v1.6.0 y anteriores) cualquier `fleetclaude`
+  dentro de tmux abria una kitty nueva y un socket `fleetN+1` — el sintoma que
+  acumulaba 6+ sockets `fleet*`.
+- **`local x` unbound bajo `set -u`**: el archivo corre con `set -euo pipefail`.
+  `local left_pane right_pane` dejaba esas vars *unbound* (no vacias), asi que la
+  rama "reutilizar sesion existente" (`--reuse`/`--session <vivo>`) reventaba con
+  `left_pane: unbound variable` al evaluar `[[ -z "$left_pane" ]]`. Se inicializan
+  explicitamente a `""` (`local left_pane="" right_pane=""`). Si tocas estas vars,
+  no vuelvas a declararlas sin valor.
 - **kitty detached (setsid)**: la ventana kitty se lanza con `setsid ... &` para
   sobrevivir al cierre de la terminal que la invoco. La ventana de Windows
   Terminal (wt.exe) ya es un proceso Windows independiente del arbol Linux, asi
@@ -128,15 +159,29 @@ al retomar el trabajo en el repo `fn_registry`.
 - **Ancho del sidebar via hooks**: `client-resized` y `window-layout-changed`
   re-fijan el pane 0 (TUI) a `--cols` columnas, porque el `attach` de kitty y el
   conmutar de Claude redistribuyen el espacio.
-- **tmux siempre; terminal (kitty/wt.exe) solo sin TTY**: `tmux` es obligatorio
+- **tmux siempre; terminal (kitty/wt.exe) solo en la ruta ventana-nueva**: `tmux`
-  (aborta != 0 si falta). Una terminal nueva (kitty o Windows Terminal) solo se
+  es obligatorio (aborta != 0 si falta). Una terminal nueva (kitty o Windows
-  necesita en la ruta sin-TTY (dentro de tmux, atajo de escritorio, cron, script),
+  Terminal) solo se necesita en la ruta ventana-nueva: `--new`, o sin TTY ni flota
-  donde abre una ventana nueva. Invocado desde una terminal interactiva fuera de
+  viva que reusar (atajo de escritorio, cron, script). Dentro de una flota viva sin
-  tmux (el caso normal del alias `fleetclaude`), reutiliza la terminal actual con
+  `--new` se reusa el contexto (ni kitty ni wt.exe). Invocado desde una terminal
-  `exec tmux attach` y no necesita ni kitty ni wt.exe.
+  interactiva fuera de tmux (el caso normal del alias `fleetclaude`), reutiliza la
+  terminal actual con `exec tmux attach` y tampoco necesita kitty ni wt.exe.
 
 ## Capability growth log
 
+- v1.7.0 (2026-06-30) — **reuse de contexto dentro de la flota + flag `--new`**.
+  Invocado sin `--new` desde dentro de una flota tmux viva (su window `console`),
+  `fleetclaude` ya NO abre una kitty nueva ni crea un perfil `fleetN+1`: trae la
+  TUI al pane/contexto actual (`fleetview show`, o `tmux -L <perfil> select-window
+  -t <perfil>:console` como fallback sin binario) y retorna 0. El perfil actual se
+  deriva de `$TMUX` (basename del socket); pasar `--session <otro>` distinto al
+  actual equivale a `--new` implicito. Nuevo flag `--new` para forzar la ruta
+  clasica (flota+ventana nueva) aun dentro de tmux. Fuera de tmux el comportamiento
+  es intacto (`exec tmux attach` reutiliza la terminal). Arregla el sintoma de que
+  lanzar `fleetclaude` dentro de una flota abria ventana kitty + socket nuevo
+  (`fleet7`, `fleet8`, ...). Fix incidental: `local left_pane="" right_pane=""`
+  (antes `local left_pane right_pane` reventaba con `unbound variable` bajo
+  `set -u` al reutilizar una sesion existente).
 - v1.6.0 (2026-06-29) — **auto-deteccion de terminal (kitty ↔ Windows Terminal)**.
   La ruta ventana-nueva ya no asume kitty: elige terminal segun el host. kitty si
   esta instalado y hay display (`$DISPLAY`/`$WAYLAND_DISPLAY`); si no, en WSL abre

bash/functions/infra/launch_fleetclaude.sh

@@ -23,6 +23,7 @@ launch_fleetclaude() {
     local cols=52
     local explicit_session=0   # 1 si el usuario pasó --session <name> a mano
     local reuse=0              # 1 si el usuario pidió --reuse (reattach al perfil principal)
+    local want_new=0          # 1 si el usuario pidió --new (forzar flota+ventana nueva)
     local T=""                # socket tmux aislado; se fija al resolver el perfil
 
     # -----------------------------------------------------------------------
@@ -46,6 +47,9 @@ launch_fleetclaude() {
             --reuse)
                 reuse=1
                 ;;
+            --new)
+                want_new=1
+                ;;
             --cols)
                 shift
                 cols="${1:-40}"
@@ -62,6 +66,11 @@ Claudes). Sin --session ni --reuse, cada invocacion abre un perfil NUEVO: usa
 el primer nombre libre de la secuencia fleet, fleet2, fleet3, ... Asi puedes
 tener varias FleetView abiertas a la vez, cada una con su flota independiente.
 
+REUSO DE CONTEXTO: si ya estas DENTRO de una flota tmux viva (p. ej. en el pane
+del orquestador), 'fleetclaude' sin args NO abre una ventana ni crea un perfil
+nuevo: trae la TUI al contexto/pane actual (equivale a 'fleetview show'). Para
+abrir explicitamente una flota aparte en una ventana nueva, usa --new.
+
 Opciones:
   --cwd <dir>       Directorio de trabajo de los panes.
                     Default: raiz del repo fn_registry (derivada dinamicamente).
@@ -69,13 +78,21 @@ Opciones:
                     Default: <repo>/apps/fleetview/fleetview
   --session <name>  Fija el perfil (socket+sesion) por nombre exacto; reutiliza
                     el existente si ya esta vivo. Sin esta opcion, perfil auto.
+                    Si se invoca DENTRO de tmux con un nombre DISTINTO al de la
+                    flota actual, equivale a --new (pides otra flota).
   --reuse           Reattach al perfil principal 'fleet' en vez de abrir uno
                     nuevo (vuelve al comportamiento idempotente clasico).
+  --new             Fuerza una flota NUEVA en una ventana NUEVA (kitty/wt.exe),
+                    incluso dentro de tmux. Es la via explicita para tener una
+                    FleetView aparte; sin este flag, dentro de tmux se reusa el
+                    contexto actual.
   --cols <n>        Ancho (columnas) del pane izquierdo. Default: 40.
   -h, --help        Muestra esta ayuda.
 
 Ejemplos:
-  launch_fleetclaude                      # perfil nuevo (fleet, luego fleet2, ...)
+  launch_fleetclaude                      # dentro de la flota: reusa el contexto;
+                                          # fuera de tmux: perfil nuevo (fleet, ...)
+  launch_fleetclaude --new                # flota+ventana nueva aunque estes en tmux
   launch_fleetclaude --reuse              # reattach a la flota principal 'fleet'
   launch_fleetclaude --session trabajo    # perfil con nombre fijo 'trabajo'
   launch_fleetclaude --cwd ~/fn_registry --cols 50
@@ -127,6 +144,45 @@ USAGE
         return 1
     fi
 
+    # -----------------------------------------------------------------------
+    # REUSO DE CONTEXTO (sin --new): si ya estamos DENTRO de una flota tmux
+    # viva, 'fleetclaude' sin args NO abre una ventana/terminal nueva ni crea
+    # un perfil fleetN+1 — trae la TUI al contexto/pane actual, igual que
+    # 'fleetview show'. El flag --new fuerza el comportamiento clasico (flota
+    # nueva en ventana nueva); --reuse mantiene su semantica historica.
+    #
+    # El perfil de la flota actual se deriva de $TMUX (el basename del socket
+    # es el nombre -L; senal fiable aunque $FLEET_SOCKET venga vacio, ver
+    # detect_fleet_context). Si se paso --session con un nombre DISTINTO al
+    # actual, es pedir OTRA flota -> se trata como --new implicito (no reusa).
+    # "Flota viva" = el socket tiene una sesion homonima con una window
+    # 'console' (la firma de una FleetView), no un tmux cualquiera.
+    # -----------------------------------------------------------------------
+    if [[ "$want_new" -eq 0 && "$reuse" -eq 0 && -n "${TMUX:-}" ]]; then
+        local current_socket target_socket
+        current_socket="$(basename "${TMUX%%,*}")"
+        target_socket="$current_socket"
+        [[ "$explicit_session" -eq 1 ]] && target_socket="$session"
+
+        if [[ "$target_socket" == "$current_socket" ]] \
+           && tmux -L "$current_socket" has-session -t "$current_socket" 2>/dev/null \
+           && tmux -L "$current_socket" list-windows -t "$current_socket" \
+                   -F '#{window_name}' 2>/dev/null | grep -qx console; then
+            # Traer la TUI al contexto actual sin abrir nada nuevo. Preferimos
+            # el binario (centraliza la politica en la app: 'fleetview show');
+            # si no esta compilado, caemos a 'select-window' directo, que es lo
+            # que 'show' hace por dentro dentro de tmux (cero dependencia).
+            if [[ -x "$bin" ]] \
+               && FLEET_SOCKET="$current_socket" FLEET_SESSION="$current_socket" \
+                  "$bin" show 2>/dev/null; then
+                return 0
+            fi
+            tmux -L "$current_socket" select-window -t "$current_socket":console
+            echo "launch_fleetclaude: flota '$current_socket' viva; TUI traida al contexto actual (sin ventana nueva)."
+            return 0
+        fi
+    fi
+
     # -----------------------------------------------------------------------
     # Resolver el PERFIL (socket+sesion tmux comparten nombre).
     #
@@ -200,7 +256,10 @@ USAGE
     # indice 1 y cualquier referencia a console.0 falla con
     # "can't find pane: 0". Los pane ID son estables e inmunes al base-index.
     # -----------------------------------------------------------------------
-    local left_pane right_pane
+    # Inicializadas a "" (no solo declaradas): bajo `set -u` una `local x` sin
+    # valor queda *unbound*, y al reutilizar una sesion existente el `[[ -z
+    # "$left_pane" ]]` de mas abajo reventaba con "unbound variable".
+    local left_pane="" right_pane=""
     if $T has-session -t "$session" 2>/dev/null; then
         echo "launch_fleetclaude: la sesion tmux '$session' ya existe; reutilizandola."
     else

docs/automatic_eda_contract.md

@@ -25,7 +25,8 @@ cabecera, y figuras/imágenes se escalan para caber enteras.
 ```
 Document  = list[Chapter]
 Chapter   = { id: str, title: str, version: str, blocks: list[Block] }
-Block     = Heading | Markdown | KVTable | DataTable | Figure | Image | Caption | Note
+Block     = Heading | Markdown | KVTable | DataTable | Figure | Image | Caption
+          | Note | Group | GlossaryEntry
 ```
 
 Importa el modelo desde `datascience.automatic_eda.model` (o
@@ -44,6 +45,10 @@ reconocido se degrada a `Note`, nunca lanza).
 | `Figure(fig=None, make=None, caption=None, height_in=None)` | una `matplotlib.figure.Figure` ya construida (`fig`) o un callable `make()->Figure` (perezoso) | se rasteriza y escala para caber entera (nunca recortada) |
 | `Image(path, caption=None, height_in=None)` | ruta a PNG/JPG | se escala para caber entera |
 | `Caption(text)` / `Note(text)` | texto auxiliar pequeño | pie/nota en gris; `Note` es además el fallback de lo desconocido |
+| `Group(blocks, title=None)` | unidad **keep-together**: sus bloques se mantienen juntos | el renderer mide el grupo entero y lo mueve completo a la página/slide siguiente si no cabe; encoge la figura para dejar sitio al título+texto. Ver §11 |
+| `GlossaryEntry(key, label, definition)` | una entrada del glosario (destino clicable) | la genera el capítulo `glosario`; registra su posición como destino de los términos marcados. Ver §11 |
+
+`Figure`/`Image` aceptan `height_in` (hint): el renderer **clampa** la figura a esa altura máxima (lo usa `Group` para encoger la figura). Toda figura escala dejando sitio a su caption en la misma página/slide; en PPTX el caption es **siempre** visible (si no se da `caption`, cae al último heading o a "Figura").
 
 ### Subset de markdown soportado (`Markdown`)
 
@@ -84,8 +89,9 @@ El orden canónico está **pre-declarado** en
 
 ```python
 CHAPTER_ORDER = [
-    "portada", "overview", "num_distr", "cat_distr", "calidad", "correlacion",
+    "portada", "overview", "analisis_llm", "num_distr", "cat_distr", "calidad",
-    "modelos", "analisis_llm", "timeseries", "geospatial", "agregacion",
+    "correlacion", "modelos", "timeseries", "geospatial", "agregacion",
+    "glosario",
 ]
 ```
 
@@ -95,6 +101,15 @@ CHAPTER_ORDER = [
 `CHAPTER_ORDER`) y aparecerá automáticamente en su posición. Esto permite que muchos
 agentes trabajen **en paralelo** sin contención: cada uno toca solo su archivo.
 
+**Dos capítulos tienen posición especial** (los gestiona `build_document`, no toques esto):
+
+- `portada`: se **construye el último** (después del cuerpo) para poder resumir el
+  análisis, pero se **coloca el primero**. Recibe `ctx['document_summary']` (ver §5) con
+  un resumen agregado del resto. Decisión del usuario: la portada refleja hallazgos.
+- `glosario`: se construye y se **coloca el último**. Lee los términos que los demás
+  capítulos registraron en `ctx['glossary']` (ver §11). Si no se registró ninguno, el
+  capítulo devuelve `None` y desaparece.
+
 Si tu capítulo usa un `<id>` que aún no está en `CHAPTER_ORDER`, añádelo en la posición
 correcta (única edición compartida; coordínala con el orquestador).
 
@@ -143,6 +158,8 @@ defensivo). Esto habilita el **seguimiento y la mejora continua por capítulo**.
 | `granularity` | "Cada fila es…" (portada). Default: derivado de `key_candidates` |
 | `quality_criteria` | criterios del score de calidad (portada) |
 | `head_rows` | `list[dict]` con `df.head` (overview). Ver §7 |
+| `glossary` | `GlossaryCollector` compartido — los capítulos registran términos en él. Lo crea `build_document`; ver §11 |
+| `document_summary` | dict con el resumen agregado del cuerpo (n_rows, n_cols, quality_score, n_numeric, n_categorical, chapter_titles, …). Lo calcula `build_document` y lo consume la portada |
 
 Un capítulo puede definir y consumir sus propias claves `ctx` — documenta cuáles en su
 docstring.
@@ -279,6 +296,109 @@ sus bloques presentes y el no-corte (texto largo intacto en la salida). Patrón:
 
 ---
 
+## 11. Glosario, keep-together y zebra (motor, fase 4a)
+
+Tres capacidades transversales del motor que **todos** los capítulos pueden usar. La 6.1
+(glosario) requiere que el capítulo coopere (registrar + marcar términos); la 6.2
+(keep-together) es opt-in por capítulo (envolver bloques en `Group`); la 6.3 (zebra) es
+automática (no hay nada que hacer).
+
+### 11.1 Glosario con términos clicables
+
+El glosario es un capítulo nuevo (`chapters/glosario.py`) que se renderiza **siempre el
+último** y lista cada término técnico que algún capítulo haya registrado. Cada aparición
+del término en el texto se vuelve un **clic real** que salta a su entrada: en PDF como
+*link annotation* interno (post-proceso con PyMuPDF, porque `PdfPages` no soporta
+hyperlinks internos), en PPTX como *slide-jump* nativo (`ppaction://hlinksldjump`).
+
+**API exacta para un capítulo (dos pasos):**
+
+1. **Registrar el término** en el colector compartido `ctx['glossary']` (un
+   `model.GlossaryCollector`, creado por `build_document` y pasado a todos los capítulos):
+
+   ```python
+   glossary = ctx.get("glossary")
+   if isinstance(glossary, model.GlossaryCollector):
+       glossary.add("entropia", "Entropía (de Shannon)", "Medida, en bits, de …")
+   ```
+
+   `add(key, label, definition)` es idempotente (la primera definición de cada `key` gana).
+   `key` debe ser `[A-Za-z0-9_]+`. Si no hay colector en `ctx` (renderizado suelto), el
+   capítulo simplemente no marca términos — degrada sin romper.
+
+2. **Marcar cada aparición** en el texto de un bloque `Markdown` con el span inline
+   `[[term:KEY]]texto visible[[/term]]`. El texto visible puede llevar `**negrita**`. El
+   marcador no altera el texto visible (se elimina como cualquier marcador inline); solo
+   añade el destino clicable.
+
+   ```python
+   # En cat_distr (ejemplo real ya implementado):
+   "La [[term:entropia]]**entropía de Shannon**[[/term]] mide cómo de repartidos…"
+   ```
+
+Eso es todo: el capítulo `glosario` recoge los términos (orden alfabético por `label`),
+emite un `GlossaryEntry` por término, y los renderers cablean los enlaces automáticamente.
+Si ningún capítulo registró términos, el glosario no aparece.
+
+**Helpers de `text_layout` (no reimplementar):** `parse_inline_rich(text)` →
+`[(texto, is_bold, term_key), …]`; `wrap_rich_terms(text, max_chars)` → líneas de esos
+spans sin corte. `strip_inline_md` ya elimina los marcadores `[[term:…]]`/`[[/term]]`.
+(Las funciones previas `parse_inline_bold` / `wrap_rich` siguen existiendo, sin términos.)
+
+**Funciones del registry que cablean los enlaces** (grupo `eda`, ya invocadas por los
+renderers; degradan en silencio si faltan): `add_pdf_internal_links_py_datascience`
+(PyMuPDF, link GOTO) y `pptx_link_run_to_slide_py_datascience` (salto a slide nativo).
+Dependencia: `pymupdf` (declarada en `python/pyproject.toml`).
+
+**Trabajo de la siguiente fase — enganchar más términos.** El mecanismo está hecho y
+probado de extremo a extremo con `entropia` (en `cat_distr`). Cada capítulo debe registrar
+y marcar SUS términos con el mismo patrón de dos pasos. Candidatos por capítulo:
+
+| Capítulo | Términos a enganchar (key sugerida) |
+|---|---|
+| `cat_distr` | `entropia` ✅ (hecho) |
+| `calidad` | `completitud`, `validez`, `consistencia` |
+| `correlacion` | `cramers_v`, `fdr` (comparaciones múltiples), método de correlación usado |
+| `modelos` | `pca`, `silhouette`, `isolation_forest` |
+| `timeseries` | `estacionariedad`, `acf_pacf`, `stl` |
+| `num_distr` | `iqr`, `curtosis`, `outlier` (vallas de Tukey) |
+
+Define la definición de cada término en su capítulo (constante local, como
+`_TERM_ENTROPIA_DEF` en `cat_distr`) y márcalo en su primera aparición.
+
+### 11.2 Keep-together: gráfico junto a su título y texto (`Group`)
+
+Para que un encabezado no quede en una página/slide y su figura en la siguiente, envuelve
+los bloques de una misma idea en un `model.Group`:
+
+```python
+blocks.append(model.Group(blocks=[
+    model.Heading(text=str(name), level=2),
+    model.Figure(make=_figura_perezosa(...), caption="…"),
+    model.Markdown(text="explicación…"),
+]))
+```
+
+El renderer **mide el grupo entero** antes de dibujar nada: si no cabe en lo que queda de
+página/slide pero cabe en una entera, lo mueve **completo** a la siguiente; y **encoge la
+figura** (vía `height_in`) lo justo para que el título + texto + figura quepan juntos. Si
+el grupo es más alto que una página entera, empieza en una nueva y fluye (degradación
+honesta, nunca corta). Ejemplo real implementado: `num_distr` envuelve cada columna
+(heading + figura histograma/boxplot + nota) en un `Group`.
+
+Recomendado para `agregacion` y cualquier capítulo donde una figura deba ir pegada a su
+título/explicación. Coste: si un capítulo inspecciona `chapter.blocks` en sus tests, ahora
+encontrará `Group`s — aplana con un helper recursivo (ver `num_distr_test.py::_flatten`).
+
+### 11.3 Zebra striping en tablas (automático)
+
+Todo `DataTable` se renderiza con **filas pares sombreadas** (gris muy suave `#f6f8fa`) y
+cabecera con su fondo propio. Es automático en PDF y PPTX; el patrón se mantiene coherente
+cuando una tabla larga se parte y repite cabecera (el índice de fila es lógico, no por
+página). No hay nada que hacer en los capítulos.
+
+---
+
 ## 10. Integración futura con `profile_table` (siguiente fase)
 
 `profile_table(emit_pdf=True)` usa hoy `render_eda_pdf` (intacto). En la siguiente fase

python/functions/datascience/__init__.py

@@ -34,6 +34,7 @@ from .theils_u import theils_u
 from .correlation_ratio import correlation_ratio
 from .mutual_info_columns import mutual_info_columns
 from .infer_fk_containment_duckdb import infer_fk_containment_duckdb
+from .detect_declared_keys_duckdb import detect_declared_keys_duckdb
 from .build_join_graph import build_join_graph
 from .association_matrix import association_matrix
 from .correlation_matrix_duckdb import correlation_matrix_duckdb
@@ -68,11 +69,15 @@ from .extract_timeseries_raw import extract_timeseries_raw
 from .build_eda_render_ctx import build_eda_render_ctx
 from .profile_datetime import profile_datetime
 from .resample_timeseries import resample_timeseries
+from .add_pdf_internal_links import add_pdf_internal_links
+from .suggest_intratable_fk_candidates import suggest_intratable_fk_candidates
 
 __all__ = [
+    "suggest_intratable_fk_candidates",
     "detect_time_column",
     "extract_timeseries_raw",
     "build_eda_render_ctx",
+    "add_pdf_internal_links",
     "profile_datetime",
     "resample_timeseries",
     "render_automatic_eda_pdf",
@@ -95,6 +100,7 @@ __all__ = [
     "correlation_ratio",
     "mutual_info_columns",
     "infer_fk_containment_duckdb",
+    "detect_declared_keys_duckdb",
     "build_join_graph",
     "association_matrix",
     "correlation_matrix_duckdb",

python/functions/datascience/add_pdf_internal_links.md

@@ -0,0 +1,85 @@
+---
+name: add_pdf_internal_links
+kind: function
+lang: py
+domain: datascience
+version: "1.0.0"
+purity: impure
+signature: "def add_pdf_internal_links(pdf_path: str, links: list) -> dict"
+description: "Postprocesa un PDF YA escrito insertando link annotations internos de tipo GOTO ('ir a') con PyMuPDF (import fitz). Pensado para PDFs generados por matplotlib PdfPages, que NO soporta hyperlinks internos: tras escribir el PDF se reabre y, por cada entrada de `links`, se añade una anotacion clicable desde un rectangulo de una pagina origen (src_page + src_rect en puntos top-left) hasta un punto de una pagina destino (dst_page + dst_point). Caso de uso tipico del grupo eda: hacer clicables los terminos de un AutomaticEDA que apuntan a su entrada en el glosario al final del documento. Estilo dict-no-throw: NUNCA lanza; valida cada link y SALTA (n_skipped++) los malformados o fuera de rango en vez de fallar. Guarda de forma segura escribiendo a un temporal en el mismo directorio y haciendo os.replace atomico (evita corromper el original). Devuelve {status:ok,n_links,n_skipped} o {status:error,error}; si pymupdf no esta disponible o el archivo no existe devuelve status error."
+tags: [eda, datascience, pdf, links, glossary, pymupdf, fitz, postprocess, python]
+uses_functions: []
+uses_types: []
+returns: []
+returns_optional: false
+error_type: "error_go_core"
+imports: []
+params:
+  - name: pdf_path
+    desc: "ruta al PDF existente (str no vacio). Se reescribe IN SITU (in-place) tras añadir los links: se guarda a un temporal `.<base>.tmp_links` en el mismo directorio y se reemplaza atomicamente con os.replace. Si no es str o no existe el archivo -> {status:error}."
+  - name: links
+    desc: "lista de dicts, uno por link a insertar. Cada dict: src_page (int 0-based de la pagina origen), src_rect ([x0,y0,x1,y1] del rectangulo clicable en PUNTOS PDF 1/72\" con origen ARRIBA-IZQUIERDA), dst_page (int 0-based de la pagina destino), dst_point ([x,y] punto destino, mismos puntos top-left). Las entradas que no son dict, con page fuera de rango [0,page_count), src_rect que no tenga 4 numeros o dst_point que no tenga 2 numeros se SALTAN (n_skipped++), no lanzan. None se trata como lista vacia."
+output: "dict (NUNCA lanza): en exito {\"status\":\"ok\",\"n_links\":int,\"n_skipped\":int} con n_links = anotaciones GOTO insertadas y n_skipped = entradas invalidas saltadas. En fallo {\"status\":\"error\",\"error\":str}: pymupdf no disponible, pdf_path no es str / no existe, links no es lista, o cualquier excepcion global (el PDF original queda intacto porque el replace solo ocurre tras un save correcto)."
+tested: true
+tests: ["test_add_goto_link_basico", "test_links_invalidos_se_saltan", "test_archivo_inexistente_devuelve_error"]
+test_file_path: "python/functions/datascience/add_pdf_internal_links_test.py"
+file_path: "python/functions/datascience/add_pdf_internal_links.py"
+---
+
+## Ejemplo
+
+```python
+import sys, os
+sys.path.insert(0, os.path.join("python", "functions"))
+from datascience import add_pdf_internal_links
+
+# Tienes un PDF ya escrito por matplotlib PdfPages (sin hyperlinks internos).
+# Quieres que el texto "Margen bruto" de la pagina 0 (rectangulo en puntos
+# top-left) salte a su entrada del glosario en la ultima pagina (indice 7).
+res = add_pdf_internal_links(
+    "reports/eda.pdf",
+    [
+        {"src_page": 0, "src_rect": [72, 120, 180, 134], "dst_page": 7, "dst_point": [72, 200]},
+        {"src_page": 0, "src_rect": [72, 140, 180, 154], "dst_page": 7, "dst_point": [72, 260]},
+    ],
+)
+# res == {"status": "ok", "n_links": 2, "n_skipped": 0}
+```
+
+## Cuando usarla
+
+Justo DESPUES de escribir un PDF con matplotlib `PdfPages` (o cualquier motor
+que no genere hyperlinks internos) cuando necesitas que ciertos terminos o
+referencias sean clicables y salten a otra pagina del mismo documento — el caso
+canonico es enlazar los terminos de un AutomaticEDA con su entrada de glosario
+al final. Es un paso de postproceso: primero generas el PDF y calculas en que
+rectangulo quedo cada termino (en puntos PDF), luego pasas esa lista a esta
+funcion para inyectar las anotaciones GOTO.
+
+## Gotchas
+
+- **Impura — reescribe el archivo IN SITU.** El PDF en `pdf_path` se reemplaza
+  por la version con los links. El guardado es seguro: escribe a un temporal
+  `.<base>.tmp_links` en el MISMO directorio y hace `os.replace` atomico tras
+  cerrar el documento, asi un fallo a mitad no corrompe el original. Aun asi,
+  conserva una copia si el PDF es valioso.
+- **Sistema de coordenadas: puntos top-left, igual que matplotlib.** PyMuPDF y
+  matplotlib (PdfPages) usan ambos PUNTOS PDF (1/72") con el origen ARRIBA-
+  IZQUIERDA, asi que los rectangulos/puntos COINCIDEN: el `src_rect` que calcules
+  con la geometria de la figura matplotlib se pasa tal cual, sin invertir el eje
+  Y. (Ojo: el espacio de datos de matplotlib SI tiene el origen abajo; lo que
+  coincide es el espacio de la PAGINA en puntos.)
+- **Indices de pagina 0-based.** `src_page` / `dst_page` son indices base 0
+  (la primera pagina es 0). Fuera del rango `[0, page_count)` el link se SALTA
+  (cuenta en `n_skipped`), no lanza.
+- **dict-no-throw, validacion por-link.** Las entradas malformadas (no dict,
+  page fuera de rango, `src_rect` sin 4 numeros, `dst_point` sin 2 numeros) se
+  saltan individualmente e incrementan `n_skipped`; el resto de links validos se
+  insertan igual. La funcion solo devuelve `{status:error}` ante fallos globales
+  (pymupdf ausente, archivo inexistente, `links` no es lista).
+- **`error_type: error_go_core` es metadata del registry, no comportamiento.**
+  Toda funcion impura debe declararlo y el indexer lo exige, pero el codigo NUNCA
+  lanza esa excepcion: degrada al dict de estado.
+- **Requiere PyMuPDF (`import fitz`).** Si no esta instalado devuelve
+  `{"status":"error","error":"pymupdf no disponible: ..."}`. En el registry el
+  venv `python/.venv` ya lo trae.

python/functions/datascience/add_pdf_internal_links.py

@@ -0,0 +1,132 @@
+"""Postprocesa un PDF existente insertando link annotations internos (GOTO).
+
+Motor: PyMuPDF (``import fitz``). Pensado para PDFs generados por matplotlib
+``PdfPages``, que no soporta hyperlinks internos: tras escribir el PDF, esta
+funcion lo reabre y le añade anotaciones "ir a" (GOTO) desde un rectangulo de
+una pagina origen hasta un punto de una pagina destino. Util para hacer
+clicables terminos que apuntan a su entrada en un glosario al final del
+documento.
+
+Estilo dict-no-throw del grupo `eda`: NUNCA lanza; devuelve un dict de estado.
+"""
+
+import os
+
+
+def add_pdf_internal_links(pdf_path: str, links: list) -> dict:
+    """Añade link annotations internos (GOTO) a un PDF ya escrito.
+
+    Postprocesa un PDF (p.ej. generado por matplotlib PdfPages, que NO soporta
+    hyperlinks internos) insertando, por cada entrada de ``links``, una
+    anotacion de tipo "ir a" desde un rectangulo de una pagina origen hasta un
+    punto de una pagina destino. Sirve para hacer clicables terminos que apuntan
+    a su entrada en un glosario al final del documento.
+
+    Args:
+        pdf_path: ruta al PDF existente (se reescribe in situ).
+        links: lista de dicts, cada uno:
+            {
+              "src_page": int,            # indice 0-based de la pagina origen
+              "src_rect": [x0,y0,x1,y1],  # rectangulo clicable, en PUNTOS PDF
+                                          # (1/72") con origen ARRIBA-IZQUIERDA
+              "dst_page": int,            # indice 0-based de la pagina destino
+              "dst_point": [x, y],        # punto destino, mismos puntos top-left
+            }
+
+    Returns:
+        dict (NUNCA lanza): {"status":"ok","n_links":int,"n_skipped":int}
+        o {"status":"error","error":str}. Si pymupdf no esta disponible o el
+        archivo no existe -> {"status":"error", ...}.
+    """
+    try:
+        try:
+            import fitz  # PyMuPDF
+        except Exception as exc:  # ImportError u otro fallo de carga
+            return {"status": "error", "error": f"pymupdf no disponible: {exc}"}
+
+        if not isinstance(pdf_path, str) or not pdf_path:
+            return {"status": "error", "error": "pdf_path debe ser una ruta no vacia"}
+        if not os.path.isfile(pdf_path):
+            return {"status": "error", "error": f"el archivo no existe: {pdf_path}"}
+
+        if links is None:
+            links = []
+        if not isinstance(links, (list, tuple)):
+            return {"status": "error", "error": "links debe ser una lista de dicts"}
+
+        doc = fitz.open(pdf_path)
+        try:
+            n_pages = doc.page_count
+            n_ok = 0
+            n_skipped = 0
+
+            for link in links:
+                if not isinstance(link, dict):
+                    n_skipped += 1
+                    continue
+
+                src_page = link.get("src_page")
+                dst_page = link.get("dst_page")
+                src_rect = link.get("src_rect")
+                dst_point = link.get("dst_point")
+
+                # src_page / dst_page: enteros 0-based en rango.
+                if not _is_int(src_page) or not _is_int(dst_page):
+                    n_skipped += 1
+                    continue
+                if not (0 <= src_page < n_pages) or not (0 <= dst_page < n_pages):
+                    n_skipped += 1
+                    continue
+
+                # src_rect: 4 numeros.
+                if not _is_num_seq(src_rect, 4):
+                    n_skipped += 1
+                    continue
+                # dst_point: 2 numeros.
+                if not _is_num_seq(dst_point, 2):
+                    n_skipped += 1
+                    continue
+
+                try:
+                    doc[int(src_page)].insert_link(
+                        {
+                            "kind": fitz.LINK_GOTO,
+                            "from": fitz.Rect(*[float(v) for v in src_rect]),
+                            "page": int(dst_page),
+                            "to": fitz.Point(*[float(v) for v in dst_point]),
+                        }
+                    )
+                    n_ok += 1
+                except Exception:
+                    n_skipped += 1
+                    continue
+
+            # Guardado seguro: escribir a temporal en el mismo directorio y
+            # reemplazar atomicamente (evita corromper el PDF original).
+            directory = os.path.dirname(os.path.abspath(pdf_path)) or "."
+            base = os.path.basename(pdf_path)
+            tmp_path = os.path.join(directory, f".{base}.tmp_links")
+            doc.save(tmp_path)
+        finally:
+            doc.close()
+
+        os.replace(tmp_path, pdf_path)
+
+        return {"status": "ok", "n_links": n_ok, "n_skipped": n_skipped}
+    except Exception as exc:  # degrada cualquier fallo a dict de error
+        return {"status": "error", "error": str(exc)}
+
+
+def _is_int(value) -> bool:
+    """True si value es un entero (no bool)."""
+    return isinstance(value, int) and not isinstance(value, bool)
+
+
+def _is_num_seq(value, length: int) -> bool:
+    """True si value es una secuencia de `length` numeros (int/float, no bool)."""
+    if not isinstance(value, (list, tuple)) or len(value) != length:
+        return False
+    for v in value:
+        if isinstance(v, bool) or not isinstance(v, (int, float)):
+            return False
+    return True

python/functions/datascience/add_pdf_internal_links_test.py

@@ -0,0 +1,77 @@
+"""Tests para add_pdf_internal_links."""
+
+import os
+import sys
+
+import pytest
+
+sys.path.insert(0, os.path.dirname(__file__))
+
+from add_pdf_internal_links import add_pdf_internal_links
+
+
+def test_add_goto_link_basico(tmp_path):
+    """Golden: un PDF de 2 paginas recibe un link GOTO de la pag 0 a la pag 1."""
+    fitz = pytest.importorskip("fitz")
+
+    # 1) PDF temporal de 2 paginas A5 (~419x595 puntos).
+    pdf = str(tmp_path / "doc.pdf")
+    doc = fitz.open()
+    doc.new_page(width=419, height=595)
+    doc.new_page(width=419, height=595)
+    doc.save(pdf)
+    doc.close()
+
+    # 2) Insertar un link interno desde la pag 0 hacia la pag 1.
+    res = add_pdf_internal_links(
+        pdf,
+        [{"src_page": 0, "src_rect": [50, 50, 200, 70], "dst_page": 1, "dst_point": [40, 40]}],
+    )
+    assert res["status"] == "ok"
+    assert res["n_links"] == 1
+    assert res["n_skipped"] == 0
+
+    # 3) Reabrir y verificar que la pag 0 tiene un link GOTO a la pag 1.
+    doc = fitz.open(pdf)
+    try:
+        links = doc[0].get_links()
+        goto = [l for l in links if l.get("kind") == fitz.LINK_GOTO and l.get("page") == 1]
+        assert len(goto) >= 1
+    finally:
+        doc.close()
+
+
+def test_links_invalidos_se_saltan(tmp_path):
+    """Edge: entradas malformadas o fuera de rango incrementan n_skipped, no lanzan."""
+    fitz = pytest.importorskip("fitz")
+
+    pdf = str(tmp_path / "doc.pdf")
+    doc = fitz.open()
+    doc.new_page(width=419, height=595)
+    doc.new_page(width=419, height=595)
+    doc.save(pdf)
+    doc.close()
+
+    res = add_pdf_internal_links(
+        pdf,
+        [
+            # valido
+            {"src_page": 0, "src_rect": [10, 10, 90, 30], "dst_page": 1, "dst_point": [20, 20]},
+            # dst_page fuera de rango
+            {"src_page": 0, "src_rect": [10, 40, 90, 60], "dst_page": 9, "dst_point": [20, 20]},
+            # src_rect con 3 numeros
+            {"src_page": 0, "src_rect": [10, 70, 90], "dst_page": 1, "dst_point": [20, 20]},
+            # no es dict
+            "no-soy-un-dict",
+        ],
+    )
+    assert res["status"] == "ok"
+    assert res["n_links"] == 1
+    assert res["n_skipped"] == 3
+
+
+def test_archivo_inexistente_devuelve_error():
+    """Error path: pdf_path inexistente -> status error sin lanzar."""
+    res = add_pdf_internal_links("/ruta/que/no/existe_xyz.pdf", [])
+    assert res["status"] == "error"
+    assert "error" in res

python/functions/datascience/automatic_eda/__init__.py

@@ -21,6 +21,9 @@ from .model import (  # noqa: F401
     Chapter,
     DataTable,
     Figure,
+    GlossaryCollector,
+    GlossaryEntry,
+    Group,
     Heading,
     Image,
     KVTable,
@@ -45,6 +48,9 @@ __all__ = [
     "Image",
     "Caption",
     "Note",
+    "Group",
+    "GlossaryEntry",
+    "GlossaryCollector",
     "Chapter",
     "as_blocks",
     "as_chapters",

python/functions/datascience/automatic_eda/chapters/agregacion.py

@@ -89,6 +89,35 @@ _DEF_MAX_CARD = 20
 _DEF_MAX_MEASURES = 4
 _DEF_TOP_N = 12
 
+# Glossary terms this chapter explains. Both appear in the always-rendered intro,
+# so they are registered and marked clickable whenever a collector is in ctx —
+# the canonical two-step pattern (see ``cat_distr``): ``glossary.add(key, label,
+# definition)`` + the inline span ``[[term:KEY]]texto[[/term]]`` in a Markdown
+# block. Mapping key -> (label, definition).
+_TERM_DEFS = {
+    "groupby": (
+        "Agrupación (split-apply-combine)",
+        "Operación de agrupación (group by): parte la tabla en grupos según los "
+        "valores de una columna categórica, aplica un cálculo (conteo, media, "
+        "mediana…) dentro de cada grupo y combina los resultados en una tabla "
+        "resumen. Es el patrón split-apply-combine."),
+    "pivot_table": (
+        "Tabla dinámica (pivot)",
+        "Tabla dinámica que cruza dos variables categóricas — una en las filas y "
+        "otra en las columnas — y rellena cada celda con un agregado (media, "
+        "suma…) de una medida numérica. Resume de un vistazo cómo interactúan las "
+        "dos categóricas sobre esa medida."),
+}
+
+
+def _term(mark: bool, key: str, text: str) -> str:
+    """Wrap ``text`` as a clickable glossary span when ``mark`` is True.
+
+    The visible text is identical with or without the marker (the renderers strip
+    it), so wrapping never changes line layout — it only adds the link.
+    """
+    return f"[[term:{key}]]{text}[[/term]]" if mark else text
+
 
 # --------------------------------------------------------------------------- #
 # Formatting helpers (mirror the other chapters' defensive style).
@@ -525,13 +554,18 @@ def _sections_live(profile: dict, ctx: dict, candidates: dict) -> list:
 # --------------------------------------------------------------------------- #
 # Entry point.
 # --------------------------------------------------------------------------- #
-def _intro_blocks() -> list:
+def _intro_blocks(gloss=None, mark_term: bool = False) -> list:
+    if gloss is not None:
+        for key, (label, definition) in _TERM_DEFS.items():
+            gloss.add(key, label, definition)
+    t_groupby = _term(mark_term, "groupby", "**por grupos** (split-apply-combine)")
+    t_pivot = _term(mark_term, "pivot_table", "**tablas dinámicas** (pivot)")
     text = (
-        "Este capítulo analiza la tabla **por grupos** (split-apply-combine): "
+        f"Este capítulo analiza la tabla {t_groupby}: "
         "elige las columnas categóricas más informativas — por su cardinalidad "
         "y relevancia, no todas contra todas, para no inflar comparaciones "
         "espurias — y resume las variables numéricas dentro de cada grupo "
-        "(conteo, media, mediana, desviación). Las **tablas dinámicas** (pivot) "
+        f"(conteo, media, mediana, desviación). Las {t_pivot} "
         "cruzan dos categóricas sobre una medida, y los **gráficos de barras** "
         "(siempre desde cero) comparan los grupos de un vistazo."
     )
@@ -556,13 +590,21 @@ def build_agregacion(profile: dict, ctx: dict):
     if not isinstance(profile, dict):
         return None
 
+    # Shared glossary collector: groupby + pivot_table live in the always-present
+    # intro, so they are registered + marked there. Degrades silently (mark_term
+    # False) when no collector is in ctx (standalone render).
+    glossary = ctx.get("glossary")
+    gloss = glossary if isinstance(glossary, model.GlossaryCollector) else None
+    mark_term = gloss is not None
+
     # Pre-computed results take precedence (offline / tests / forward-compat).
     pre = ctx.get("aggregations")
     if _is_dict(pre) and (pre.get("groupby") or pre.get("pivots")):
         sections = _sections_from_precomputed(pre)
         if not sections:
             return None
-        blocks = _intro_blocks() + sections + _insights_section(ctx)
+        blocks = (_intro_blocks(gloss, mark_term) + sections
+                  + _insights_section(ctx))
         return model.Chapter(id=CHAPTER_ID, title=CHAPTER_TITLE,
                              version=CHAPTER_VERSION, blocks=blocks)
 
@@ -583,10 +625,11 @@ def build_agregacion(profile: dict, ctx: dict):
             "crudos. Pasa ctx['db_path'] + ctx['table'] (para el cálculo "
             "push-down en DuckDB) o ctx['aggregations'] ya precalculado. "
             f"Columnas categóricas candidatas: {keys or '—'}.")
-        blocks = _intro_blocks() + [note] + _insights_section(ctx)
+        blocks = (_intro_blocks(gloss, mark_term) + [note]
+                  + _insights_section(ctx))
         return model.Chapter(id=CHAPTER_ID, title=CHAPTER_TITLE,
                              version=CHAPTER_VERSION, blocks=blocks)
 
-    blocks = _intro_blocks() + sections + _insights_section(ctx)
+    blocks = _intro_blocks(gloss, mark_term) + sections + _insights_section(ctx)
     return model.Chapter(id=CHAPTER_ID, title=CHAPTER_TITLE,
                          version=CHAPTER_VERSION, blocks=blocks)

python/functions/datascience/automatic_eda/chapters/agregacion_test.py

@@ -254,3 +254,25 @@ def test_anti_corte_muchos_grupos_y_texto_largo():
         # First, middle and last words of the long paragraph all present.
         for i in (0, 60, 119):
             assert f"palabra{i}" in txt
+
+
+def test_glosario_engancha_groupby_y_pivot():
+    """Mejora 4b: la agrupación (split-apply-combine) y la tabla dinámica (pivot)
+    se registran en el colector compartido y se marcan clicables en el cuerpo.
+    Sin colector en ctx, el capítulo degrada y no marca nada."""
+    from datascience.automatic_eda.model import GlossaryCollector
+
+    g = GlossaryCollector()
+    ctx = dict(_ctx_precomputed())
+    ctx["glossary"] = g
+    ch = build_agregacion(_profile(), ctx)
+    assert ch is not None
+    keys = {t["key"] for t in g.terms()}
+    assert {"groupby", "pivot_table"} <= keys
+    body = " ".join(b.text for b in ch.blocks if b.kind == "markdown")
+    assert "[[term:groupby]]" in body and "[[term:pivot_table]]" in body
+
+    # Sin colector: degrada limpio (ningún marcador en el cuerpo).
+    ch2 = build_agregacion(_profile(), _ctx_precomputed())
+    body2 = " ".join(b.text for b in ch2.blocks if b.kind == "markdown")
+    assert "[[term:" not in body2

python/functions/datascience/automatic_eda/chapters/analisis_llm.py

@@ -42,7 +42,11 @@ from __future__ import annotations
 
 from .. import model
 
-CHAPTER_VERSION = "1.0.0"
+# 1.1.0: drop the duplicated section labels — the dictionary and PII DataTables
+# no longer carry a ``title`` (the section Heading labels them once, per the
+# OVERVIEW pattern in the contract). The data-dictionary column already reads
+# "Significado de negocio".
+CHAPTER_VERSION = "1.1.0"
 CHAPTER_ID = "analisis_llm"
 CHAPTER_TITLE = "Análisis LLM"
 
@@ -118,6 +122,11 @@ def _dictionary_block(llm: dict):
     Columns: Columna / Descripción / Significado de negocio / Unidad. The
     paginator splits this by rows repeating the header and wraps long cells, so a
     long dictionary (many columns) never gets cut.
+
+    The block carries **no** ``title``: the section is labelled once by the
+    ``Heading`` that ``build_analisis_llm`` appends right before it (the canonical
+    OVERVIEW pattern, contract §8). Giving the table its own ``title`` too would
+    print "Diccionario de datos" twice in a row.
     """
     entries = llm.get("dictionary")
     if not isinstance(entries, (list, tuple)) or not entries:
@@ -137,7 +146,7 @@ def _dictionary_block(llm: dict):
         ])
     if not rows:
         return None
-    return model.DataTable(header=header, rows=rows, title="Diccionario de datos")
+    return model.DataTable(header=header, rows=rows)
 
 
 def _analyses_blocks(llm: dict) -> list:
@@ -159,7 +168,12 @@ def _cleaning_blocks(llm: dict) -> list:
 
 
 def _pii_block(llm: dict):
-    """DataTable for PII/GDPR findings, or None if absent/empty."""
+    """DataTable for PII/GDPR findings, or None if absent/empty.
+
+    Like the dictionary block, it carries **no** ``title`` (the ``Heading`` in
+    ``build_analisis_llm`` labels the section once); it keeps its ``note`` with
+    the orientative-detection caveat, which the renderers print under the table.
+    """
     entries = llm.get("pii")
     if not isinstance(entries, (list, tuple)) or not entries:
         return None
@@ -176,7 +190,7 @@ def _pii_block(llm: dict):
     if not rows:
         return None
     return model.DataTable(
-        header=header, rows=rows, title="Datos personales (PII / RGPD)",
+        header=header, rows=rows,
         note="detección automática orientativa — revisar antes de tratar los datos")
 
 

python/functions/datascience/automatic_eda/chapters/analisis_llm_test.py

@@ -24,7 +24,7 @@ from pptx import Presentation
 from datascience.automatic_eda.chapters.analisis_llm import (
     build_analisis_llm, CHAPTER_VERSION)
 from datascience.automatic_eda.chapters_registry import build_document
-from datascience.automatic_eda.model import Chapter, DataTable
+from datascience.automatic_eda.model import Chapter, DataTable, Heading
 from datascience.render_automatic_eda_pdf import render_automatic_eda_pdf
 from datascience.render_automatic_eda_pptx import render_automatic_eda_pptx
 
@@ -117,6 +117,45 @@ def test_golden_build_y_render_pdf_pptx():
         assert "DESCTOKEN" in ptx
 
 
+def test_sin_rotulos_duplicados_y_significado_de_negocio():
+    """The dictionary / PII sections must be labelled ONCE.
+
+    Regression for the duplicated 'Diccionario de datos' and 'Datos personales
+    (PII / RGPD)' headings (each section used to print its label twice: a Heading
+    plus the DataTable's own title). The fix drops the DataTable title and keeps
+    a single Heading — the OVERVIEW pattern. The data-dictionary column header is
+    also pinned to the exact text 'Significado de negocio'.
+    """
+    ch = build_analisis_llm(_profile(), {})
+    assert ch is not None
+
+    # Structure: section labels come from Headings; tables carry no title.
+    headings = [b.text for b in ch.blocks if isinstance(b, Heading)]
+    assert headings.count("Diccionario de datos") == 1
+    assert headings.count("Datos personales (PII / RGPD)") == 1
+    for b in ch.blocks:
+        if isinstance(b, DataTable):
+            assert not b.title, f"DataTable should not duplicate the label: {b.title!r}"
+
+    # The data dictionary's third column reads exactly 'Significado de negocio'.
+    dicts = [b for b in ch.blocks if isinstance(b, DataTable) and "Descripción" in b.header]
+    assert dicts, "expected the data-dictionary DataTable"
+    assert dicts[0].header == ["Columna", "Descripción", "Significado de negocio", "Unidad"]
+
+    # The PII table keeps its orientative-detection note.
+    pii = [b for b in ch.blocks if isinstance(b, DataTable) and b.header == ["Columna", "Tipo", "Severidad"]]
+    assert pii and pii[0].note and "orientativa" in pii[0].note
+
+    # Render: each label appears exactly once across the whole document (the only
+    # 'Diccionario de datos' / 'Datos personales' producer is this chapter).
+    with tempfile.TemporaryDirectory() as d:
+        out_pdf = os.path.join(d, "eda.pdf")
+        render_automatic_eda_pdf(_profile(), out_pdf, {"title": "EDA — ventas"})
+        txt = _pdf_text(out_pdf)
+        assert txt.count("Diccionario de datos") == 1
+        assert txt.count("Datos personales") == 1
+
+
 def test_orden_capitulo_junto_a_overview():
     chapters = build_document(_profile(), {})
     ids = [c.id for c in chapters]

python/functions/datascience/automatic_eda/chapters/calidad.py

@@ -1,22 +1,26 @@
 """Data-quality chapter (CALIDAD) for AutomaticEDA.
 
 Builds the quality chapter from a ``TableProfile`` of the ``eda`` group. The
-chapter answers, in Spanish and as tables, the three things the user asked for:
+chapter implements the quality model of report 2046:
 
-1. **En qué se basa la calidad** — an intro paragraph explaining the criteria and
+1. **En qué se basa la calidad** — an intro paragraph explaining the two scored
-   their weights (completeness, validity, consistency) before any number, plus a
+   dimensions and their weights (completitud 60%, validez 40%) plus the
-   table-level summary (global score and aggregates).
+   table-level row uniqueness, BEFORE any number, and stating explicitly that
+   outliers are reported as observations and do **not** lower the score. The
+   criteria terms (calidad de datos, completitud, validez, unicidad de registro)
+   are hooked into the shared glossary as clickable jumps.
 2. **Scores por columna** — a table with, per column, the total quality score and
-   its breakdown into completeness / validity / consistency.
+   its breakdown into completeness / validity (no consistency dimension).
-3. **Problemas en español** — a second table listing, per column, the readable
+3. **Problemas de calidad** — a table listing ONLY real quality defects
-   issues in Spanish (kept separate from the type ``flags``).
+   (nulls, empty cells, values not conforming to their type/semantics).
+4. **Observaciones analíticas** — a SEPARATE table for outliers, constant
+   columns, high-cardinality ids and strong skew, with an explicit note that
+   these do not affect the score.
 
-The breakdown and the issues are NOT recomputed here: they come from the registry
+The breakdown, issues and observations are NOT recomputed here: they come from
-function ``column_quality_score`` (group ``eda``), which already derives
+the registry function ``column_quality_score`` (group ``eda``), which derives
-``{score, completeness, validity, consistency, issues}`` from the ColumnProfile.
+``{score, completeness, validity, dimensions, applicable, issues,
-This chapter is render-only — it consumes that function and lays the result out
+observations}`` from the ColumnProfile. This chapter is render-only.
-as model blocks; the renderers paginate tables (splitting by rows, repeating the
-header) and wrap long cells so nothing is ever cut.
 
 Contract: build_<id>(profile, ctx) -> Chapter | None ; CHAPTER_VERSION = "x.y.z".
 """
@@ -33,28 +37,47 @@ try:  # pragma: no cover - import wiring
 except Exception:  # noqa: BLE001 - never let an import error abort the document.
     _column_quality_score = None
 
-CHAPTER_VERSION = "1.0.0"
+CHAPTER_VERSION = "2.0.0"
 CHAPTER_ID = "calidad"
 CHAPTER_TITLE = "Calidad"
 
-# Weights mirror column_quality_score: completeness 0.5, validity 0.3,
+# Glossary terms this chapter explains (report 2046 §6). Registered in the shared
-# consistency 0.2. Kept here only to render the human explanation; the actual
+# collector and marked clickable on their first appearance (contract §11.1).
-# numbers always come from the function so the two never drift in computation.
+_TERMS = {
-_CRITERIA_INTRO = (
+    "calidad_datos": (
-    "La calidad de cada columna es un score de 0 a 100 que combina tres "
+        "Calidad de datos (score 0-100)",
-    "criterios, cada uno con un peso:\n\n"
+        "Mide hasta qué punto los datos están presentes y son utilizables tal "
-    "- **Completitud (peso 50%)**: proporción de valores presentes (sin nulos "
+        "cual, no si son «buenos para el análisis». Se compone solo de "
-    "ni vacíos). Una columna con muchos nulos baja de score.\n"
+        "dimensiones medibles automáticamente desde el perfil de la tabla, sin "
-    "- **Validez (peso 30%)**: los valores son coherentes con su tipo y rango "
+        "fuente externa de verdad: completitud (60%), validez (40%, cuando es "
-    "esperado (penaliza outliers y semánticas declaradas que no coinciden).\n"
+        "medible) y, a nivel de tabla, unicidad de registro. Los valores "
-    "- **Consistencia (peso 20%)**: la columna aporta información útil (penaliza "
+        "atípicos NO bajan la calidad: se listan aparte como observaciones.",
-    "columnas constantes o identificadores de cardinalidad muy alta).\n\n"
+    ),
-    "Score = 100 × (0,5·completitud + 0,3·validez + 0,2·consistencia). "
+    "completitud": (
-    "Los problemas detectados por columna se listan en español más abajo."
+        "Completitud",
-)
+        "Proporción de valores realmente presentes en una columna (1 − % de "
+        "nulos; en texto, las celdas vacías también cuentan como faltantes). Los "
+        "nulos y vacíos bajan el score porque falta información que debería "
+        "estar. Pesa el 60% del score de columna.",
+    ),
+    "validez": (
+        "Validez",
+        "Proporción de valores que encajan con su tipo o formato esperado: un "
+        "número que parsea, una fecha legible, un email con forma de email. Los "
+        "valores que no parsean a su tipo bajan el score. Si la columna es texto "
+        "libre sin formato esperado, la validez no se puede medir y el score se "
+        "basa solo en la completitud. Pesa el 40% del score cuando es medible.",
+    ),
+    "unicidad_registro": (
+        "Unicidad de registro",
+        "A nivel de tabla, las filas duplicadas restan calidad al conjunto "
+        "(1 − % de filas duplicadas). Es distinta de que una columna no-clave "
+        "repita valores, que no es un defecto de calidad.",
+    ),
+}
 
-# Cap for the joined issues cell so a single row never grows taller than a page;
+# Cap for the joined cell so a single row never grows taller than a page; the
-# the remainder is summarized as "(+N más)" instead of being silently dropped.
+# remainder is summarized as "(+N más)" instead of being silently dropped.
 _ISSUES_MAXLEN = 160
 
 
@@ -82,12 +105,19 @@ def _fmt_unit_pct(value) -> str:
         return str(value)
 
 
+def _fmt_validity(value) -> str:
+    """Validity is ``None`` when not applicable: show ``n/a`` not a fake 0%."""
+    if value is None:
+        return "n/a"
+    return _fmt_unit_pct(value)
+
+
 def _quality_of(col: dict) -> dict:
-    """Return ``{score, completeness, validity, consistency, issues}`` for a column.
+    """Return the quality dict for a column.
 
     Uses the registry ``column_quality_score`` when available; otherwise falls
     back to the per-column ``quality_score`` already in the profile (number only,
-    empty breakdown/issues). Never raises.
+    empty breakdown/issues/observations). Never raises.
     """
     if not isinstance(col, dict):
         col = {}
@@ -98,26 +128,25 @@ def _quality_of(col: dict) -> dict:
                 return res
         except Exception:  # noqa: BLE001 - degrade instead of aborting.
             pass
-    # Fallback: only the final score is available pre-computed in the profile.
     return {
         "score": col.get("quality_score"),
         "completeness": None,
         "validity": None,
-        "consistency": None,
         "issues": [],
+        "observations": [],
     }
 
 
-def _join_issues(issues) -> str:
+def _join_cells(items) -> str:
-    """Join Spanish issue strings into one cell, truncating overly long lists.
+    """Join Spanish strings into one cell, truncating overly long lists.
 
-    The renderer wraps cell text, but a column with many long issues could make a
+    The renderer wraps cell text, but a column with many long entries could make
-    single row taller than a whole page; cap the length and append ``(+N más)``
+    a single row taller than a whole page; cap the length and append ``(+N más)``
-    so the count of hidden issues is honest rather than silently lost.
+    so the count of hidden entries is honest rather than silently lost.
     """
-    if not isinstance(issues, (list, tuple)) or not issues:
+    if not isinstance(items, (list, tuple)) or not items:
         return ""
-    parts = [model._safe_str(i).strip() for i in issues]
+    parts = [model._safe_str(i).strip() for i in items]
     parts = [p for p in parts if p]
     if not parts:
         return ""
@@ -142,6 +171,33 @@ def _columns_with_quality(profile: dict):
             yield c, _quality_of(c)
 
 
+def _fmt_unit_pct_or_pct(value) -> str:
+    """Format a value that may be a 0-1 fraction or an already-0-100 percentage."""
+    try:
+        num = float(value)
+    except (TypeError, ValueError):
+        return model._safe_str(value)
+    if num != num:  # NaN
+        return "—"
+    pct = num * 100 if num <= 1.0 else num
+    text = f"{pct:.1f}".rstrip("0").rstrip(".")
+    return f"{text}%"
+
+
+def _row_uniqueness(profile: dict):
+    """Return row uniqueness (1 - duplicate_pct) in [0,1], or None if unknown."""
+    dup = profile.get("duplicate_pct")
+    if dup is None:
+        return None
+    try:
+        d = float(dup)
+    except (TypeError, ValueError):
+        return None
+    if d > 1.0:  # tolerate a 0-100 scale
+        d = d / 100.0
+    return max(0.0, min(1.0, 1.0 - d))
+
+
 def _summary_block(profile: dict, evaluated: list):
     """Table-level KVTable: global score and quality aggregates."""
     rows = []
@@ -153,14 +209,15 @@ def _summary_block(profile: dict, evaluated: list):
              if isinstance(q.get("completeness"), (int, float))]
     vals = [q.get("validity") for _, q in evaluated
             if isinstance(q.get("validity"), (int, float))]
-    cons = [q.get("consistency") for _, q in evaluated
-            if isinstance(q.get("consistency"), (int, float))]
     if comps:
         rows.append(("Completitud media", _fmt_unit_pct(sum(comps) / len(comps))))
     if vals:
-        rows.append(("Validez media", _fmt_unit_pct(sum(vals) / len(vals))))
+        rows.append(("Validez media (donde aplica)",
-    if cons:
+                     _fmt_unit_pct(sum(vals) / len(vals))))
-        rows.append(("Consistencia media", _fmt_unit_pct(sum(cons) / len(cons))))
+
+    ru = _row_uniqueness(profile)
+    if ru is not None:
+        rows.append(("Unicidad de registro", _fmt_unit_pct(ru)))
 
     n_problem = sum(1 for _, q in evaluated if q.get("issues"))
     rows.append(("Columnas con problemas", str(n_problem)))
@@ -182,22 +239,9 @@ def _summary_block(profile: dict, evaluated: list):
     return model.KVTable(rows=rows, title="Resumen de calidad")
 
 
-def _fmt_unit_pct_or_pct(value) -> str:
-    """Format a value that may be a 0-1 fraction or an already-0-100 percentage."""
-    try:
-        num = float(value)
-    except (TypeError, ValueError):
-        return model._safe_str(value)
-    if num != num:  # NaN
-        return "—"
-    pct = num * 100 if num <= 1.0 else num
-    text = f"{pct:.1f}".rstrip("0").rstrip(".")
-    return f"{text}%"
-
-
 def _scores_block(evaluated: list):
-    """DataTable with per-column score and its three-criteria breakdown."""
+    """DataTable with per-column score and its completeness/validity breakdown."""
-    header = ["Columna", "Calidad", "Completitud", "Validez", "Consistencia"]
+    header = ["Columna", "Calidad", "Completitud", "Validez"]
     rows = []
     # Worst columns first so the reader sees the problems at the top.
     ordered = sorted(
@@ -210,22 +254,22 @@ def _scores_block(evaluated: list):
             col.get("name") or "(col)",
             _fmt_score(q.get("score")),
             _fmt_unit_pct(q.get("completeness")),
-            _fmt_unit_pct(q.get("validity")),
+            _fmt_validity(q.get("validity")),
-            _fmt_unit_pct(q.get("consistency")),
         ])
     if not rows:
         return None
     return model.DataTable(header=header, rows=rows,
                            title="Scores de calidad por columna",
-                           note="0 = peor, 100 = mejor; ordenado de peor a mejor")
+                           note="0 = peor, 100 = mejor; «n/a» = dimensión no "
+                                "medible; ordenado de peor a mejor")
 
 
 def _issues_block(evaluated: list):
-    """DataTable listing Spanish issues per column, or a Note when there are none."""
+    """DataTable listing ONLY real quality defects per column, or a Note."""
-    header = ["Columna", "Problemas detectados (español)"]
+    header = ["Columna", "Problemas de calidad (español)"]
     rows = []
     for col, q in evaluated:
-        joined = _join_issues(q.get("issues"))
+        joined = _join_cells(q.get("issues"))
         if joined:
             rows.append([col.get("name") or "(col)", joined])
     if not rows:
@@ -235,6 +279,63 @@ def _issues_block(evaluated: list):
                            title="Problemas de calidad por columna")
 
 
+def _observations_block(evaluated: list):
+    """DataTable listing analytical observations per column, or None.
+
+    Observations (outliers, constant columns, ids, strong skew) are NOT quality
+    defects: they do not affect the score. Returned as a separate table from the
+    issues so the report never presents a legitimate outlier as a problem.
+    """
+    header = ["Columna", "Observaciones analíticas"]
+    rows = []
+    for col, q in evaluated:
+        joined = _join_cells(q.get("observations"))
+        if joined:
+            rows.append([col.get("name") or "(col)", joined])
+    if not rows:
+        return None
+    return model.DataTable(
+        header=header, rows=rows,
+        title="Observaciones analíticas por columna",
+        note="No son defectos de calidad y NO afectan al score; orientan el "
+             "análisis (atípicos, columnas constantes, identificadores).")
+
+
+def _term(key: str, label: str, mark: bool) -> str:
+    """Render a term as a clickable glossary span when marking is enabled."""
+    if mark:
+        return f"[[term:{key}]]**{label}**[[/term]]"
+    return f"**{label}**"
+
+
+def _criteria_intro(mark: bool) -> str:
+    """Intro paragraph explaining the two scored dimensions and the principle."""
+    calidad = _term("calidad_datos", "calidad de datos", mark)
+    completitud = _term("completitud", "Completitud (peso 60%)", mark)
+    validez = _term("validez", "Validez (peso 40%, cuando es medible)", mark)
+    unicidad = _term("unicidad_registro", "unicidad de registro", mark)
+    return (
+        f"La {calidad} de cada columna es un score de 0 a 100 que combina solo "
+        "dimensiones medibles desde el perfil de la tabla, sin fuente externa "
+        "de verdad:\n\n"
+        f"- {completitud}: proporción de valores presentes (1 − % de nulos; en "
+        "texto, las celdas vacías cuentan como faltantes). Los nulos y vacíos "
+        "bajan el score.\n"
+        f"- {validez}: proporción de valores que encajan con su tipo o formato "
+        "(un número que parsea, una fecha legible, un email con forma de email). "
+        "Si una columna es texto libre sin formato esperado, la validez no se "
+        "mide y el score se basa solo en la completitud.\n\n"
+        f"Score de columna = 100 × (0,6·completitud + 0,4·validez), "
+        "renormalizado cuando la validez no aplica. A nivel de tabla se añade "
+        f"la {unicidad} (1 − % de filas duplicadas).\n\n"
+        "**Los valores atípicos (outliers) NO bajan la calidad.** Un valor "
+        "extremo puede ser real y correcto; detectar atípicos es parte del "
+        "análisis de la distribución, no un juicio de corrección. Por eso, junto "
+        "con las columnas constantes y los identificadores, se listan aparte "
+        "como **observaciones analíticas** que no afectan al score."
+    )
+
+
 def build_calidad(profile: dict, ctx: dict):
     """Build the data-quality Chapter, or None if the profile has no columns.
 
@@ -250,17 +351,35 @@ def build_calidad(profile: dict, ctx: dict):
     if not evaluated:
         return None  # no columns to score -> chapter does not apply.
 
+    # Register the criteria terms in the shared glossary (if present) and mark
+    # their first appearance clickable. Contract §11.1.
+    glossary = ctx.get("glossary")
+    mark = False
+    if isinstance(glossary, model.GlossaryCollector):
+        for key, (label, definition) in _TERMS.items():
+            glossary.add(key, label, definition)
+        mark = True
+
     blocks = [
         model.Heading(text="Cómo se calcula la calidad", level=2),
-        model.Markdown(text=_CRITERIA_INTRO),
+        model.Markdown(text=_criteria_intro(mark)),
         _summary_block(profile, evaluated),
         model.Heading(text="Scores por columna", level=2),
     ]
     scores = _scores_block(evaluated)
     if scores is not None:
         blocks.append(scores)
-    blocks.append(model.Heading(text="Problemas detectados", level=2))
+
+    blocks.append(model.Heading(text="Problemas de calidad", level=2))
     blocks.append(_issues_block(evaluated))
 
+    observations = _observations_block(evaluated)
+    if observations is not None:
+        blocks.append(model.Heading(text="Observaciones analíticas", level=2))
+        blocks.append(model.Note(
+            "Las observaciones siguientes NO son defectos de calidad y no "
+            "afectan al score: son señales para orientar el análisis."))
+        blocks.append(observations)
+
     return model.Chapter(id=CHAPTER_ID, title=CHAPTER_TITLE,
                          version=CHAPTER_VERSION, blocks=blocks)

python/functions/datascience/automatic_eda/chapters/calidad_test.py

@@ -1,11 +1,12 @@
-"""Tests for the CALIDAD chapter — DoD: golden + edges + anti-cut.
+"""Tests for the CALIDAD chapter — DoD: golden + edges + anti-cut + glossary.
 
 Self-contained: builds synthetic TableProfiles (no DuckDB) so the suite is fast
-and deterministic. Verifies that the chapter explains the quality criteria, shows
+and deterministic. Verifies the report-2046 quality model: the chapter explains
-per-column scores with the completeness/validity/consistency breakdown, lists the
+the two scored dimensions (completitud 60% / validez 40%), shows per-column
-issues in Spanish (separate from the type flags), returns None when it does not
+scores without a consistency column, keeps quality DEFECTS (issues) separate
-apply, and that a wide profile with long names renders to PDF and PPTX without
+from analytical OBSERVATIONS (outliers, constant, ids), hooks the criteria terms
-cutting any cell text (long content wraps, it is never truncated).
+into the glossary, returns None when it does not apply, and renders a wide
+profile to PDF and PPTX without cutting any cell text.
 """
 
 import os
@@ -20,28 +21,30 @@ from datascience.automatic_eda.chapters.calidad import (
     CHAPTER_VERSION,
 )
 from datascience.automatic_eda import build_document, render_pdf, render_pptx
+from datascience.automatic_eda import model
 
 
 def _profile() -> dict:
     """A small profile with one column per quality problem (nulls, outliers,
-    constant, high-cardinality id) plus one clean column."""
+    constant, high-cardinality id) plus one clean column. ``outlier_pct`` is in
+    the 0-100 scale that describe_numeric actually emits."""
     return {
         "table": "demo",
-        "quality_score": 72.5,
+        "quality_score": 82.0,
         "duplicate_pct": 0.04,
         "null_cell_pct": 0.11,
         "constant_cols": ["flag_const"],
         "all_null_cols": [],
         "columns": [
-            {"name": "edad", "inferred_type": "integer", "null_pct": 0.2,
+            {"name": "edad", "inferred_type": "numeric", "null_pct": 0.2,
-             "numeric": {"outlier_pct": 0.15, "min": 0, "max": 99},
+             "n_rows": 100, "unique_pct": 0.5,
-             "quality_score": 60},
+             "numeric": {"outlier_pct": 15.0, "min": 0, "max": 99}},
             {"name": "nombre", "inferred_type": "text", "null_pct": 0.0,
-             "unique_pct": 0.98, "quality_score": 80},
+             "unique_pct": 0.98, "flags": ["possible_id"]},
             {"name": "flag_const", "inferred_type": "text", "null_pct": 0.0,
-             "flags": ["constant"], "quality_score": 50},
+             "unique_pct": 0.01, "flags": ["constant"]},
-            {"name": "limpia", "inferred_type": "float", "null_pct": 0.0,
+            {"name": "limpia", "inferred_type": "numeric", "null_pct": 0.0,
-             "numeric": {"outlier_pct": 0.0}, "quality_score": 100},
+             "unique_pct": 0.5, "numeric": {"outlier_pct": 0.0}},
         ],
     }
 
@@ -50,16 +53,9 @@ def _tables(chapter):
     return [b for b in chapter.blocks if getattr(b, "kind", None) == "data_table"]
 
 
-def _scores_table(chapter):
+def _table_by_title(chapter, needle):
     for t in _tables(chapter):
-        if "Scores" in (t.title or ""):
+        if needle in (t.title or ""):
-            return t
-    return None
-
-
-def _issues_table(chapter):
-    for t in _tables(chapter):
-        if "Problemas" in (t.title or ""):
             return t
     return None
 
@@ -73,41 +69,84 @@ def test_golden_chapter_estructura_y_version():
     assert ch.id == "calidad"
     assert ch.version == CHAPTER_VERSION
     kinds = [b.kind for b in ch.blocks]
-    # intro heading + markdown criteria + summary kv + scores table + issues table
     assert "markdown" in kinds and "kv_table" in kinds and "data_table" in kinds
 
 
-def test_golden_intro_explica_criterios_y_pesos():
+def test_golden_intro_explica_dos_dimensiones_y_pesos():
     ch = build_calidad(_profile(), {})
     intro = [b for b in ch.blocks if b.kind == "markdown"][0].text
-    for needle in ("Completitud", "Validez", "Consistencia",
+    for needle in ("Completitud", "Validez", "60%", "40%",
-                   "50%", "30%", "20%"):
+                   "unicidad de registro"):
         assert needle in intro, f"falta {needle!r} en la intro de criterios"
+    # El principio: los outliers NO bajan la calidad.
+    assert "atípicos" in intro and "NO bajan" in intro
+    # Ya no se menciona la dimensión consistencia eliminada.
+    assert "20%" not in intro
 
 
-def test_golden_scores_incluyen_desglose_por_criterio():
+def test_golden_scores_sin_columna_consistencia():
     ch = build_calidad(_profile(), {})
-    scores = _scores_table(ch)
+    scores = _table_by_title(ch, "Scores")
     assert scores is not None
-    assert scores.header == ["Columna", "Calidad", "Completitud",
+    assert scores.header == ["Columna", "Calidad", "Completitud", "Validez"]
-                             "Validez", "Consistencia"]
+    assert "Consistencia" not in scores.header
-    # 4 columns scored, none dropped.
     assert len(scores.rows) == 4
     names = {r[0] for r in scores.rows}
     assert names == {"edad", "nombre", "flag_const", "limpia"}
 
 
-def test_golden_issues_en_espanol_separados_de_flags():
+def test_golden_outliers_en_observaciones_no_en_problemas():
     ch = build_calidad(_profile(), {})
-    issues = _issues_table(ch)
+    problemas = _table_by_title(ch, "Problemas de calidad")
-    assert issues is not None
+    observaciones = _table_by_title(ch, "Observaciones")
-    flat = " | ".join(" ".join(r) for r in issues.rows)
+    assert problemas is not None
-    assert "nulos" in flat            # completeness issue (ES)
+    assert observaciones is not None
-    assert "outliers" in flat         # validity issue (ES)
+
-    assert "columna constante" in flat
+    problemas_txt = " | ".join(" ".join(r) for r in problemas.rows)
-    assert "posible id de alta cardinalidad" in flat
+    observaciones_txt = " | ".join(" ".join(r) for r in observaciones.rows)
-    # The raw type flag string must NOT leak as a "problem".
+
-    assert "constant" not in flat or "columna constante" in flat
+    # Los nulos SÍ son problema de calidad.
+    assert "nulos" in problemas_txt
+    # Los outliers NO aparecen como problema...
+    assert "atípic" not in problemas_txt and "outlier" not in problemas_txt
+    # ...sino como observación analítica.
+    assert "atípic" in observaciones_txt
+    # Constante e id: observaciones, no problemas.
+    assert "constante" in observaciones_txt
+    assert "identificador" in observaciones_txt
+    assert "constante" not in problemas_txt
+
+
+def test_golden_score_columna_limpia_es_100():
+    """Columna sin nulos, numérica nativa: score 100 aunque tenga (o no) outliers."""
+    ch = build_calidad(_profile(), {})
+    scores = _table_by_title(ch, "Scores")
+    by_name = {r[0]: r for r in scores.rows}
+    assert by_name["limpia"][1] == "100 / 100"
+    # edad: 20% nulos -> 100*(0.6*0.8 + 0.4*1.0) = 88; los outliers no bajan nada.
+    assert by_name["edad"][1] == "88 / 100"
+
+
+# --------------------------------------------------------------------------- #
+# Glosario (contrato §11.1)
+# --------------------------------------------------------------------------- #
+def test_glosario_registra_los_cuatro_terminos_y_marca_clicable():
+    glossary = model.GlossaryCollector()
+    ch = build_calidad(_profile(), {"glossary": glossary})
+    for key in ("calidad_datos", "completitud", "validez", "unicidad_registro"):
+        assert glossary.has(key), f"término {key!r} no registrado en el glosario"
+    intro = [b for b in ch.blocks if b.kind == "markdown"][0].text
+    # Con colector presente, la primera aparición se marca clicable.
+    assert "[[term:completitud]]" in intro
+    assert "[[term:validez]]" in intro
+    assert "[[term:calidad_datos]]" in intro
+    assert "[[term:unicidad_registro]]" in intro
+
+
+def test_sin_glosario_no_marca_terminos():
+    ch = build_calidad(_profile(), {})  # ctx sin glossary
+    intro = [b for b in ch.blocks if b.kind == "markdown"][0].text
+    assert "[[term:" not in intro
 
 
 # --------------------------------------------------------------------------- #
@@ -124,17 +163,17 @@ def test_edge_perfil_limpio_sin_problemas_usa_nota():
     prof = {
         "quality_score": 100,
         "columns": [
-            {"name": "a", "inferred_type": "float", "null_pct": 0.0,
+            {"name": "a", "inferred_type": "numeric", "null_pct": 0.0,
-             "numeric": {"outlier_pct": 0.0}},
+             "unique_pct": 0.5, "numeric": {"outlier_pct": 0.0}},
-            {"name": "b", "inferred_type": "float", "null_pct": 0.0,
+            {"name": "b", "inferred_type": "numeric", "null_pct": 0.0,
-             "numeric": {"outlier_pct": 0.0}},
+             "unique_pct": 0.5, "numeric": {"outlier_pct": 0.0}},
         ],
     }
     ch = build_calidad(prof, {})
     assert ch is not None
-    assert _issues_table(ch) is None  # no issues table
+    assert _table_by_title(ch, "Problemas de calidad") is None  # no issues table
     notes = [b for b in ch.blocks if b.kind == "note"]
-    assert notes and "No se detectaron problemas" in notes[0].text
+    assert any("No se detectaron problemas" in n.text for n in notes)
 
 
 # --------------------------------------------------------------------------- #
@@ -143,44 +182,42 @@ def test_edge_perfil_limpio_sin_problemas_usa_nota():
 def _wide_profile(ncols: int = 22) -> dict:
     cols = [
         {"name": "identificador_unico_de_transaccion_con_nombre_muy_largo",
-         "inferred_type": "text", "null_pct": 0.0, "unique_pct": 0.99},
+         "inferred_type": "text", "null_pct": 0.0, "unique_pct": 0.99,
+         "flags": ["possible_id"]},
         {"name": "columna_constante_sin_ninguna_variacion_de_valor",
-         "inferred_type": "text", "null_pct": 0.0, "flags": ["constant"]},
+         "inferred_type": "text", "null_pct": 0.0, "unique_pct": 0.01,
+         "flags": ["constant"]},
     ]
     for k in range(ncols - 2):
         cols.append({
             "name": f"metrica_numerica_de_negocio_{k:02d}_con_nombre_largo",
-            "inferred_type": "float", "null_pct": 0.1 + (k % 3) * 0.05,
+            "inferred_type": "numeric", "null_pct": 0.1 + (k % 3) * 0.05,
-            "numeric": {"outlier_pct": 0.08, "min": 0, "max": 1000},
+            "unique_pct": 0.5,
+            "numeric": {"outlier_pct": 8.0, "min": 0, "max": 1000},
         })
-    return {"table": "ancha", "quality_score": 70.0, "columns": cols}
+    return {"table": "ancha", "quality_score": 70.0, "duplicate_pct": 0.0,
+            "columns": cols}
 
 
 def test_anticut_pdf_y_pptx_no_truncan_nombres_largos():
     prof = _wide_profile(22)
     full = build_document(prof, {"dataset_name": "ancha"})
     assert any(c.id == "calidad" for c in full)
-    # Render ONLY the calidad chapter so the anti-cut assertions are scoped to
-    # this chapter (other chapters, e.g. portada, legitimately contain '…').
     chapters = [c for c in full if c.id == "calidad"]
     long_name = "metrica_numerica_de_negocio_00_con_nombre_largo"
     with tempfile.TemporaryDirectory() as d:
         pdf = os.path.join(d, "q.pdf")
         pptx = os.path.join(d, "q.pptx")
         rp = render_pdf(chapters, pdf, {"title": "EDA"})
-        rx = render_pptx(chapters, pptx, {"title": "EDA"})
+        render_pptx(chapters, pptx, {"title": "EDA"})
         assert os.path.exists(pdf) and os.path.exists(pptx)
-        # The wide table forces pagination across several pages/slides.
         assert (rp or {}).get("n_pages", 0) >= 2
 
-        # PDF: the long name survives whole once wraps (spaces/newlines) removed,
-        # and there is no truncation marker.
         pdf_txt = "".join((pg.extract_text() or "") for pg in PdfReader(pdf).pages)
         assert "…" not in pdf_txt and "..." not in pdf_txt
         norm = re.sub(r"\s+", "", pdf_txt)
         assert long_name in norm, "el nombre largo se cortó en el PDF"
 
-        # PPTX: long name present in some cell, untruncated.
         allt = []
         for s in Presentation(pptx).slides:
             for sh in s.shapes:

python/functions/datascience/automatic_eda/chapters/cat_distr.py

@@ -33,10 +33,23 @@ import math
 
 from .. import model
 
-CHAPTER_VERSION = "1.0.0"
+CHAPTER_VERSION = "1.1.0"
 CHAPTER_ID = "cat_distr"
 CHAPTER_TITLE = "Distribuciones categóricas"
 
+# Glossary term this chapter explains. Registered in the shared collector and
+# marked clickable on its first appearance (end-to-end glossary example —
+# mejora 6). Other chapters hook their own terms the same way (see the contract).
+_TERM_ENTROPIA_KEY = "entropia"
+_TERM_ENTROPIA_LABEL = "Entropía (de Shannon)"
+_TERM_ENTROPIA_DEF = (
+    "Medida, en bits, de cómo de repartidos están los valores de una columna "
+    "categórica. Vale 0 cuando una sola categoría concentra todas las filas "
+    "(máxima previsibilidad) y alcanza su máximo, log2(k) para k categorías "
+    "distintas, cuando todas aparecen por igual (máxima diversidad). La entropía "
+    "normalizada (entropía dividida por su máximo) la lleva al rango 0–1 para "
+    "comparar columnas con distinto número de categorías.")
+
 # Cap the number of categorical columns rendered to keep the document bounded;
 # the rest are summarized in a closing note (no silent truncation).
 MAX_COLS = 40
@@ -337,10 +350,14 @@ def _topk_table(cat: dict):
                            note=note)
 
 
-def _intro_blocks(n_rows):
+def _intro_blocks(n_rows, mark_term: bool = False):
     total = _fmt_int(n_rows)
+    # Mark the first appearance of the term as a clickable glossary jump when the
+    # term was registered (mark_term). The visible text is identical either way.
+    entropia = ("[[term:entropia]]**entropía de Shannon**[[/term]]" if mark_term
+                else "**entropía de Shannon**")
     text = (
-        "La **entropía de Shannon** mide cómo de repartidos están los valores de "
+        f"La {entropia} mide cómo de repartidos están los valores de "
         "una columna categórica, en bits. Vale 0 cuando una sola categoría "
         "concentra todas las filas (máxima previsibilidad) y alcanza su máximo, "
         "log2(k) para k categorías distintas, cuando todas aparecen por igual "
@@ -370,7 +387,15 @@ def build_cat_distr(profile: dict, ctx: dict):
         return None
 
     n_rows = profile.get("n_rows")
-    blocks = list(_intro_blocks(n_rows))
+    # Register "entropía" in the shared glossary collector (if present) and mark
+    # its first appearance clickable. End-to-end glossary example (mejora 6).
+    glossary = ctx.get("glossary")
+    mark_term = False
+    if isinstance(glossary, model.GlossaryCollector):
+        glossary.add(_TERM_ENTROPIA_KEY, _TERM_ENTROPIA_LABEL,
+                     _TERM_ENTROPIA_DEF)
+        mark_term = True
+    blocks = list(_intro_blocks(n_rows, mark_term=mark_term))
 
     rendered = cat_cols[:MAX_COLS]
     for col in rendered:

python/functions/datascience/automatic_eda/chapters/correlacion.py

@@ -47,6 +47,53 @@ _MAX_MATRIX_LABELS = 16
 # How many pairs to show in each of the top-positive / top-negative tables.
 _TOP_N = 10
 
+# Glossary terms this chapter explains. Each is registered in the shared
+# collector (ctx['glossary']) and marked clickable on its first appearance in the
+# body — the canonical two-step pattern (see ``cat_distr`` for the reference
+# implementation): ``glossary.add(key, label, definition)`` + the inline span
+# ``[[term:KEY]]texto visible[[/term]]`` in a Markdown block. Mapping key ->
+# (label, definition). ``fdr`` is only registered when the FDR summary is present.
+_TERM_DEFS = {
+    "pearson": (
+        "Pearson (coeficiente r)",
+        "Coeficiente de correlación lineal de Pearson (r) entre dos variables "
+        "numéricas. Va de −1 (relación lineal inversa perfecta) a +1 (directa "
+        "perfecta); 0 indica ausencia de relación lineal. Sólo capta relaciones "
+        "lineales, por eso lleva signo."),
+    "spearman": (
+        "Spearman (correlación de rangos)",
+        "Correlación de rangos de Spearman: el coeficiente de Pearson calculado "
+        "sobre los puestos (rangos) de los valores en vez de sus magnitudes. Mide "
+        "relaciones monótonas (no necesariamente lineales), va de −1 a +1 y es "
+        "robusta frente a valores atípicos."),
+    "cramers_v": (
+        "Cramér's V",
+        "Medida de asociación entre dos variables categóricas, derivada del "
+        "estadístico chi-cuadrado y normalizada al rango 0–1 (0 = independientes, "
+        "1 = asociación total). No tiene signo: sólo mide la intensidad."),
+    "correlation_ratio": (
+        "Razón de correlación (η)",
+        "Razón de correlación (eta) entre una variable numérica y una "
+        "categórica: la fracción de la varianza de la numérica explicada por los "
+        "grupos de la categórica. Va de 0 (los grupos no explican nada) a 1 (la "
+        "explican toda); no tiene signo."),
+    "fdr": (
+        "Comparaciones múltiples (FDR)",
+        "Al evaluar muchos pares a la vez, algunos parecen significativos por "
+        "puro azar. La corrección por tasa de falsos descubrimientos (FDR, "
+        "Benjamini-Hochberg) ajusta los p-valores para controlar la proporción "
+        "esperada de falsos positivos entre los pares declarados significativos."),
+}
+
+
+def _term(mark: bool, key: str, text: str) -> str:
+    """Wrap ``text`` as a clickable glossary span when ``mark`` is True.
+
+    The visible text is identical with or without the marker (the renderers strip
+    the marker), so wrapping never changes line layout — it only adds the link.
+    """
+    return f"[[term:{key}]]{text}[[/term]]" if mark else text
+
 
 def _is_num(v) -> bool:
     """True for a real, finite int/float (not bool, not NaN/inf)."""
@@ -245,7 +292,7 @@ def _methods_block(corr: dict):
     return model.KVTable(rows=rows, title="Métodos de asociación")
 
 
-def _fdr_text(corr: dict) -> str | None:
+def _fdr_text(corr: dict, mark_term: bool = False) -> str | None:
     """One-line summary of the multiple-testing (FDR) correction, or None."""
     mt = corr.get("multiple_testing")
     if not isinstance(mt, dict) or not mt:
@@ -254,7 +301,8 @@ def _fdr_text(corr: dict) -> str | None:
     alpha = mt.get("alpha")
     n_tests = mt.get("n_tests")
     n_rej = mt.get("n_rejected")
-    parts = [f"Corrección por comparaciones múltiples ({method}"]
+    multi = _term(mark_term, "fdr", "comparaciones múltiples")
+    parts = [f"Corrección por {multi} ({method}"]
     if _is_num(alpha):
         parts[0] += f", α={float(alpha):g}"
     parts[0] += ")."
@@ -289,13 +337,31 @@ def build_correlacion(profile: dict, ctx: dict):
 
     blocks: list = []
 
-    # Intro: what this chapter shows and how to read the sign.
+    # Register the always-present method terms in the shared glossary and mark
+    # their first appearance clickable (the FDR term is registered lazily below,
+    # only when the FDR summary is actually emitted). Degrades silently when no
+    # collector is in ctx (standalone render) — mark_term stays False.
+    glossary = ctx.get("glossary")
+    gloss = glossary if isinstance(glossary, model.GlossaryCollector) else None
+    mark_term = gloss is not None
+    if gloss is not None:
+        for key in ("pearson", "spearman", "cramers_v", "correlation_ratio"):
+            label, definition = _TERM_DEFS[key]
+            gloss.add(key, label, definition)
+
+    # Intro: what this chapter shows and how to read the sign. Build the marked
+    # method names as locals first (avoids backslash-in-f-string for "Cramér's V").
+    t_pearson = _term(mark_term, "pearson", "Pearson")
+    t_spearman = _term(mark_term, "spearman", "Spearman")
+    t_cramers = _term(mark_term, "cramers_v", "Cramér's V")
+    t_corr_ratio = _term(mark_term, "correlation_ratio", "razón de correlación")
     blocks.append(model.Markdown(text=(
         "Asociación entre columnas. Cada par se evalúa con la métrica adecuada a "
-        "sus tipos (Pearson/Spearman entre numéricas — con **signo**; Cramér's V "
+        f"sus tipos ({t_pearson}/{t_spearman} entre numéricas — con **signo**; "
-        "entre categóricas; razón de correlación num-categórica; información mutua "
+        f"{t_cramers} entre categóricas; {t_corr_ratio} num-categórica; "
-        "como medida común no lineal). Sólo las correlaciones **num-num** tienen "
+        "información mutua como medida común no lineal). Sólo las correlaciones "
-        "dirección: por eso los pares **negativos** son siempre num-num.")))
+        "**num-num** tienen dirección: por eso los pares **negativos** son siempre "
+        "num-num.")))
 
     # 1) Association matrix (heatmap).
     labels, trimmed = _ordered_labels(pairs)
@@ -337,9 +403,13 @@ def build_correlacion(profile: dict, ctx: dict):
             "no estacionarias y pueden ser espurias (Granger–Newbold). Compáralas "
             "sobre los retornos/diferencias antes de interpretarlas.")))
 
-    # 4) FDR summary + methods legend.
+    # 4) FDR summary + methods legend. Register the FDR term only when its
-    fdr_text = _fdr_text(corr)
+    # summary is emitted, so the glossary never lists an unreferenced entry.
+    fdr_text = _fdr_text(corr, mark_term=mark_term)
     if fdr_text:
+        if gloss is not None:
+            label, definition = _TERM_DEFS["fdr"]
+            gloss.add("fdr", label, definition)
         blocks.append(model.Markdown(text=fdr_text))
     methods = _methods_block(corr)
     if methods is not None:

python/functions/datascience/automatic_eda/chapters/correlacion_test.py

@@ -173,3 +173,25 @@ def test_anticorte_matriz_ancha_y_etiquetas_largas_no_se_cortan():
         assert rx["path"] == pptx and os.path.exists(pptx) and rx["n_slides"] >= 1
         # A short, unbreakable fragment of the long label survives the wrap.
         assert "azufre" in _pdf_text(pdf)
+
+
+def test_glosario_engancha_metodos_y_fdr():
+    """Mejora 4b: los métodos de correlación (Pearson, Spearman, Cramér's V,
+    razón de correlación) y la corrección por comparaciones múltiples (FDR) se
+    registran en el colector compartido y se marcan clicables en el cuerpo. Sin
+    colector en ctx, el capítulo degrada y no marca nada."""
+    from datascience.automatic_eda.model import GlossaryCollector
+
+    g = GlossaryCollector()
+    ch = build_correlacion(_profile(), {"glossary": g})
+    assert ch is not None
+    keys = {t["key"] for t in g.terms()}
+    assert {"pearson", "spearman", "cramers_v", "correlation_ratio", "fdr"} <= keys
+    body = " ".join(b.text for b in ch.blocks if b.kind == "markdown")
+    for k in ("pearson", "spearman", "cramers_v", "correlation_ratio", "fdr"):
+        assert f"[[term:{k}]]" in body, k
+
+    # Sin colector: degrada limpio (ningún marcador en el cuerpo).
+    ch2 = build_correlacion(_profile(), {})
+    body2 = " ".join(b.text for b in ch2.blocks if b.kind == "markdown")
+    assert "[[term:" not in body2

python/functions/datascience/automatic_eda/chapters/glosario.py

@@ -0,0 +1,47 @@
+"""Glossary chapter (GLOSARIO) — always the last chapter, clickable terms.
+
+Renders one entry per glossary term that the other chapters registered during
+the document build through ``ctx['glossary'].add(key, label, definition)`` (see
+``GlossaryCollector`` in ``model.py``). Each entry is a clickable destination:
+every in-text appearance a chapter marked with ``[[term:key]]texto[[/term]]``
+becomes a real jump to its entry here — PDF link annotations (PyMuPDF) and PPTX
+native slide jumps, both wired by the renderers.
+
+Returns ``None`` when no term was registered (there is nothing to show), so the
+chapter simply disappears from documents that did not mark any term.
+
+Contract: build_<id>(profile, ctx) -> Chapter | None ; CHAPTER_VERSION = "x.y.z".
+"""
+
+from __future__ import annotations
+
+from .. import model
+
+CHAPTER_VERSION = "1.0.0"
+CHAPTER_ID = "glosario"
+CHAPTER_TITLE = "Glosario"
+
+
+def build_glosario(profile: dict, ctx: dict):
+    """Build the glossary Chapter from the shared collector, or None if empty."""
+    ctx = ctx or {}
+    glossary = ctx.get("glossary")
+    if not isinstance(glossary, model.GlossaryCollector) or not glossary:
+        return None
+
+    blocks = [
+        model.Heading(text="Glosario de términos", level=1),
+        model.Markdown(text=(
+            "Definición de los términos técnicos que aparecen en el informe. "
+            "Cada término va resaltado en el texto y, al pulsarlo, salta a su "
+            "definición en esta sección.")),
+    ]
+    # One clickable destination per term, alphabetically by visible label.
+    for term in glossary.terms(by="label"):
+        blocks.append(model.GlossaryEntry(
+            key=model._safe_str(term.get("key")),
+            label=model._safe_str(term.get("label")),
+            definition=model._safe_str(term.get("definition"))))
+
+    return model.Chapter(id=CHAPTER_ID, title=CHAPTER_TITLE,
+                         version=CHAPTER_VERSION, blocks=blocks)

python/functions/datascience/automatic_eda/chapters/modelos.py

@@ -55,6 +55,62 @@ _CLUSTER_COLORS = [
     "#edc948", "#b07aa1", "#ff9da7", "#9c755f", "#bab0ac",
 ]
 
+# Glossary terms this chapter explains. Each is registered in the shared
+# collector (ctx['glossary']) and marked clickable on its first appearance — the
+# canonical two-step pattern (see ``cat_distr``): ``glossary.add(key, label,
+# definition)`` + the inline span ``[[term:KEY]]texto[[/term]]`` in a Markdown
+# block. A term is registered only when its section is actually rendered, so the
+# glossary never lists an entry no in-text appearance points to.
+_TERM_DEFS = {
+    "zscore": (
+        "Estandarización z-score",
+        "Transformación que lleva cada columna numérica a media 0 y desviación "
+        "típica 1: a cada valor le resta la media de su columna y lo divide por "
+        "la desviación típica. Así variables con escalas muy distintas (euros "
+        "frente a un ratio 0–1) pesan por igual en las distancias y la varianza."),
+    "pca": (
+        "PCA (componentes principales)",
+        "El análisis de componentes principales resume muchas variables "
+        "numéricas correlacionadas en pocos ejes nuevos (componentes), "
+        "ortogonales entre sí y ordenados por la cantidad de varianza que "
+        "capturan. Permite ver la estructura de los datos en 2D y saber cuántas "
+        "dimensiones bastan para explicarlos."),
+    "kmeans": (
+        "KMeans (segmentación)",
+        "Algoritmo de agrupamiento no supervisado que reparte las filas en k "
+        "segmentos: asigna cada fila al centro (centroide) más cercano y recoloca "
+        "los centroides de forma iterativa hasta minimizar la distancia interna "
+        "de cada grupo. Aquí k se elige automáticamente."),
+    "silhouette": (
+        "Coeficiente de silueta (silhouette)",
+        "Métrica de calidad de un agrupamiento, en el rango −1 a 1: para cada "
+        "fila compara cómo de cerca está de su propio segmento frente al segmento "
+        "vecino más próximo. Cuanto más alto el promedio, más compactos y "
+        "separados están los segmentos."),
+    "isolation_forest": (
+        "Isolation Forest (anomalías)",
+        "Algoritmo de detección de anomalías multivariante: construye árboles que "
+        "parten el espacio con cortes aleatorios y mide cuántos cortes hacen "
+        "falta para aislar cada fila. Las filas raras se aíslan con muy pocos "
+        "cortes y se marcan como outliers según un umbral de contaminación."),
+}
+
+
+def _term(mark: bool, key: str, text: str) -> str:
+    """Wrap ``text`` as a clickable glossary span when ``mark`` is True.
+
+    The visible text is identical with or without the marker (the renderers strip
+    it), so wrapping never changes line layout — it only adds the link.
+    """
+    return f"[[term:{key}]]{text}[[/term]]" if mark else text
+
+
+def _register(gloss, key: str) -> None:
+    """Register term ``key`` in the collector (idempotent); no-op if gloss None."""
+    if gloss is not None:
+        label, definition = _TERM_DEFS[key]
+        gloss.add(key, label, definition)
+
 
 # --------------------------------------------------------------------------- #
 # Formatting helpers (mirror the overview chapter's defensive style).
@@ -252,34 +308,37 @@ def _make_cluster_scatter(projection: dict):
 # --------------------------------------------------------------------------- #
 # Section builders. Each returns a list of blocks (possibly empty).
 # --------------------------------------------------------------------------- #
-def _normalization_intro() -> list:
+def _normalization_intro(gloss=None, mark_term: bool = False) -> list:
+    _register(gloss, "zscore")
+    zscore = _term(mark_term, "zscore", "**estandarizan con z-score**")
     text = (
         "Estos modelos son **no supervisados**: buscan estructura latente sin "
         "una variable objetivo. Antes de aplicarlos, todas las columnas "
-        "numéricas se **estandarizan con z-score** (cada valor menos la media, "
+        f"numéricas se {zscore} (cada valor menos la media, dividido por la "
-        "dividido por la desviación típica). Sin esta normalización, una "
+        "desviación típica). Sin esta normalización, una variable con escala "
-        "variable con escala grande (p.ej. ingresos en euros) dominaría las "
+        "grande (p.ej. ingresos en euros) dominaría las distancias y la varianza "
-        "distancias y la varianza frente a otra de escala pequeña (p.ej. un "
+        "frente a otra de escala pequeña (p.ej. un ratio entre 0 y 1), sesgando "
-        "ratio entre 0 y 1), sesgando tanto el PCA como el KMeans. Tras la "
+        "tanto el PCA como el KMeans. Tras la estandarización todas las variables "
-        "estandarización todas las variables pesan por igual."
+        "pesan por igual."
     )
     return [model.Heading(text="Modelos no supervisados", level=1),
             model.Markdown(text=text)]
 
 
-def _pca_section(pca: dict) -> list:
+def _pca_section(pca: dict, gloss=None, mark_term: bool = False) -> list:
     if not _is_dict(pca) or not pca.get("explained_variance_ratio"):
         return []
+    _register(gloss, "pca")
     blocks = [model.Heading(text="PCA — varianza explicada", level=2)]
 
     n_used = pca.get("n_rows_used")
     n_feat = pca.get("n_features")
     intro = (
-        f"El PCA resume {_fmt_num(n_feat)} variables numéricas en componentes "
+        f"El {_term(mark_term, 'pca', 'PCA')} resume {_fmt_num(n_feat)} variables "
-        f"ortogonales ordenados por la varianza que capturan "
+        "numéricas en componentes ortogonales ordenados por la varianza que "
-        f"({_fmt_num(n_used)} filas usadas tras eliminar nulos). El gráfico de "
+        f"capturan ({_fmt_num(n_used)} filas usadas tras eliminar nulos). El "
-        "sedimentación (scree) muestra cuánta varianza aporta cada componente y "
+        "gráfico de sedimentación (scree) muestra cuánta varianza aporta cada "
-        "su acumulado: un codo marca cuántos componentes bastan."
+        "componente y su acumulado: un codo marca cuántos componentes bastan."
     )
     blocks.append(model.Markdown(text=intro))
 
@@ -325,11 +384,14 @@ def _pca_section(pca: dict) -> list:
     return blocks
 
 
-def _kmeans_section(kmeans: dict, projection: dict, titles) -> list:
+def _kmeans_section(kmeans: dict, projection: dict, titles,
+                    gloss=None, mark_term: bool = False) -> list:
     has_km = _is_dict(kmeans) and kmeans.get("best_k")
     has_proj = _is_dict(projection) and projection.get("points")
     if not has_km and not has_proj:
         return []
+    _register(gloss, "kmeans")
+    _register(gloss, "silhouette")
 
     blocks = [model.Heading(text="Segmentación (KMeans)", level=2)]
 
@@ -337,9 +399,11 @@ def _kmeans_section(kmeans: dict, projection: dict, titles) -> list:
     sil = (projection or {}).get("silhouette")
     if sil is None:
         sil = (kmeans or {}).get("silhouette")
+    t_kmeans = _term(mark_term, "kmeans", "KMeans")
+    t_sil = _term(mark_term, "silhouette", "*silhouette*")
     intro = (
-        f"KMeans agrupa las filas en **{_fmt_num(best_k)} segmentos** elegidos "
+        f"{t_kmeans} agrupa las filas en **{_fmt_num(best_k)} segmentos** "
-        "automáticamente maximizando el coeficiente de *silhouette* "
+        f"elegidos automáticamente maximizando el coeficiente de {t_sil} "
         f"(**{_fmt_num(sil)}**, rango −1 a 1: cuanto más alto, segmentos más "
         "compactos y separados). Los segmentos se proyectan sobre el plano de "
         "los dos primeros componentes principales para visualizarlos."
@@ -394,16 +458,18 @@ def _kmeans_section(kmeans: dict, projection: dict, titles) -> list:
     return blocks
 
 
-def _outliers_section(outliers: dict) -> list:
+def _outliers_section(outliers: dict, gloss=None, mark_term: bool = False) -> list:
     if not _is_dict(outliers) or outliers.get("n_outliers") is None:
         return []
     if outliers.get("note") and not outliers.get("n_rows_used"):
         # insufficient data — nothing meaningful to show.
         return []
+    _register(gloss, "isolation_forest")
     blocks = [model.Heading(text="Detección de anomalías (Isolation Forest)",
                             level=2)]
+    isof = _term(mark_term, "isolation_forest", "**Isolation Forest**")
     explain = (
-        "**Isolation Forest** detecta filas anómalas de forma *multivariante*: "
+        f"{isof} detecta filas anómalas de forma *multivariante*: "
         "construye árboles que parten el espacio con cortes aleatorios y mide "
         "cuántos cortes hacen falta para aislar cada fila. Las filas raras "
         "(combinaciones de valores poco frecuentes considerando **todas las "
@@ -484,15 +550,21 @@ def build_modelos(profile: dict, ctx: dict):
         (kmeans and kmeans.get("best_k")) or (projection and projection.get("points"))
     ) else None
 
+    # Shared glossary collector: terms are registered + marked clickable inside
+    # each section, only when that section actually renders (no orphan entries).
+    glossary = ctx.get("glossary")
+    gloss = glossary if isinstance(glossary, model.GlossaryCollector) else None
+    mark_term = gloss is not None
+
     sections = []
-    sections += _pca_section(pca) if pca else []
+    sections += _pca_section(pca, gloss, mark_term) if pca else []
-    sections += _kmeans_section(kmeans, projection, titles)
+    sections += _kmeans_section(kmeans, projection, titles, gloss, mark_term)
-    sections += _outliers_section(outliers) if outliers else []
+    sections += _outliers_section(outliers, gloss, mark_term) if outliers else []
     sections += _normality_section(normality) if normality else []
 
     if not sections:
         return None  # models block present but nothing renderable.
 
-    blocks = _normalization_intro() + sections
+    blocks = _normalization_intro(gloss, mark_term) + sections
     return model.Chapter(id=CHAPTER_ID, title=CHAPTER_TITLE,
                          version=CHAPTER_VERSION, blocks=blocks)

python/functions/datascience/automatic_eda/chapters/modelos_test.py

@@ -257,3 +257,26 @@ def test_anticortes_tabla_normalidad_larga_no_corta():
         # Every column name survives (wrapped/split, never truncated).
         for i in (0, 19, 39):
             assert f"col_{i}" in txt
+
+
+def test_glosario_engancha_terminos_modelos():
+    """Mejora 4b: PCA, KMeans, silhouette, Isolation Forest y la estandarización
+    z-score se registran en el colector compartido y se marcan clicables en el
+    cuerpo. Sin colector en ctx, el capítulo degrada y no marca nada."""
+    from datascience.automatic_eda.model import GlossaryCollector
+
+    g = GlossaryCollector()
+    ctx = dict(_ctx_full())
+    ctx["glossary"] = g
+    ch = build_modelos(_profile(), ctx)
+    assert ch is not None
+    keys = {t["key"] for t in g.terms()}
+    assert {"zscore", "pca", "kmeans", "silhouette", "isolation_forest"} <= keys
+    body = " ".join(b.text for b in ch.blocks if b.kind == "markdown")
+    for k in ("zscore", "pca", "kmeans", "silhouette", "isolation_forest"):
+        assert f"[[term:{k}]]" in body, k
+
+    # Sin colector: degrada limpio (ningún marcador en el cuerpo).
+    ch2 = build_modelos(_profile(), _ctx_full())
+    body2 = " ".join(b.text for b in ch2.blocks if b.kind == "markdown")
+    assert "[[term:" not in body2

python/functions/datascience/automatic_eda/chapters/num_distr.py

@@ -1,9 +1,10 @@
 """Numeric distributions chapter (NUM DISTR) for AutomaticEDA.
 
 For every numeric column the chapter draws, as a single indivisible figure, a
-histogram with the **mean, median and ±1σ band drawn as reference lines** and a
+histogram with the **mean, median and ±1σ band drawn as reference lines** (the
-**Tukey boxplot right below it** sharing the same X axis — exactly the user
+legend reports the numeric value of the mean, the median **and the standard
-requirement for this chapter. Each figure is emitted as a lazy ``Figure`` block
+deviation σ**) and a **Tukey boxplot right below it** sharing the same X axis —
+exactly the user requirement for this chapter. Each figure is emitted as a lazy ``Figure`` block
 so the renderers rasterize and scale it to fit a whole page/slide and nothing is
 ever cut; columns with many numerics simply flow across pages as small
 multiples.
@@ -34,7 +35,7 @@ try:
 except Exception:  # noqa: BLE001 — keep the chapter importable no matter what.
     build_boxplot_stats = None  # type: ignore[assignment]
 
-CHAPTER_VERSION = "1.0.0"
+CHAPTER_VERSION = "1.2.0"
 CHAPTER_ID = "num_distr"
 CHAPTER_TITLE = "Distribuciones numéricas"
 
@@ -140,9 +141,11 @@ def _make_hist_box(name: str, numeric: dict, box: dict):
     std = numeric.get("std")
 
     # ±1σ band first (behind the lines), then median (solid) and mean (dashed).
+    # The band's legend entry also reports the numeric value of the standard
+    # deviation, so the reader sees mean, median AND σ at a glance.
     if mean is not None and std is not None and std > 0:
         ax_h.axvspan(mean - std, mean + std, color="#f0c27b", alpha=0.22,
-                     zorder=1, label="±1σ")
+                     zorder=1, label=f"±1σ (σ = {_fmt_num(std)})")
     if median is not None:
         ax_h.axvline(median, color="#2e8b57", linestyle="-", linewidth=1.6,
                      zorder=4, label=f"mediana = {_fmt_num(median)}")
@@ -152,7 +155,19 @@ def _make_hist_box(name: str, numeric: dict, box: dict):
 
     ax_h.set_ylabel("frecuencia", fontsize=8)
     ax_h.tick_params(labelsize=7)
-    ax_h.legend(fontsize=6.5, loc="upper right", framealpha=0.85)
+    # Always surface σ in the legend: if the ±1σ band could not be drawn (no mean
+    # or std<=0) but σ is still known, add a label-only proxy handle so the value
+    # of the standard deviation is reported regardless of the band.
+    handles, labels = ax_h.get_legend_handles_labels()
+    if std is not None and not any("σ =" in lbl for lbl in labels):
+        from matplotlib.lines import Line2D
+        proxy = Line2D([], [], linestyle="none", marker="",
+                       label=f"σ = {_fmt_num(std)}")
+        handles.append(proxy)
+        labels.append(f"σ = {_fmt_num(std)}")
+    if handles:
+        ax_h.legend(handles, labels, fontsize=6.5, loc="upper right",
+                    framealpha=0.85)
     for spine in ("top", "right"):
         ax_h.spines[spine].set_visible(False)
 
@@ -278,12 +293,17 @@ def build_num_distr(profile: dict, ctx: dict):
                 box = build_boxplot_stats(numeric) or {}
             except Exception:  # noqa: BLE001 — degrade, never raise.
                 box = {}
-        blocks.append(model.Heading(text=str(name), level=2))
+        # Keep the column heading, its figure and its stats note together on the
-        blocks.append(model.Figure(
+        # same page/slide (mejora 3 — keep-together): the renderers measure the
-            make=_figure_maker(name, numeric, box),
+        # whole Group and move it whole when it would not fit.
-            caption=f"Distribución de «{name}» — histograma (media/mediana/±σ) "
+        blocks.append(model.Group(blocks=[
-                    f"y boxplot."))
+            model.Heading(text=str(name), level=2),
-        blocks.append(model.Markdown(text=_stats_note(name, numeric, box)))
+            model.Figure(
+                make=_figure_maker(name, numeric, box),
+                caption=f"Distribución de «{name}» — histograma "
+                        f"(media/mediana/±σ) y boxplot."),
+            model.Markdown(text=_stats_note(name, numeric, box)),
+        ]))
 
     return model.Chapter(id=CHAPTER_ID, title=CHAPTER_TITLE,
                          version=CHAPTER_VERSION, blocks=blocks)

python/functions/datascience/automatic_eda/chapters/num_distr_test.py

@@ -65,19 +65,33 @@ def _pdf_text(path: str) -> str:
     return re.sub(r"\s+", " ", txt)
 
 
+def _flatten(blocks):
+    """Expand keep-together Groups so the per-column heading/figure/markdown are
+    inspectable as a flat block list (the chapter wraps each column in a Group)."""
+    out = []
+    for b in blocks:
+        if getattr(b, "kind", "") == "group":
+            out.extend(_flatten(getattr(b, "blocks", []) or []))
+        else:
+            out.append(b)
+    return out
+
+
 def test_golden_chapter_estructura_y_bloques():
     ch = build_num_distr(_profile(n_numeric=2), {})
     assert ch is not None
     assert ch.id == "num_distr"
     assert ch.version == CHAPTER_VERSION
-    kinds = [b.kind for b in ch.blocks]
+    # Per-column blocks are wrapped in keep-together Groups: flatten to inspect.
+    flat = _flatten(ch.blocks)
+    kinds = [b.kind for b in flat]
     # Heading + intro Markdown, then per column: Heading + Figure + Markdown.
     assert kinds[0] == "heading"
     assert kinds[1] == "markdown"
     assert kinds.count("figure") == 2          # one figure per numeric column.
     assert kinds.count("heading") == 1 + 2     # chapter title + one per column.
     # Each figure has a lazy maker that produces a real matplotlib figure.
-    figs = [b for b in ch.blocks if b.kind == "figure"]
+    figs = [b for b in flat if b.kind == "figure"]
     fig = figs[0].make()
     assert fig is not None
     # Two stacked axes: histogram + boxplot share the figure.
@@ -90,7 +104,8 @@ def test_golden_media_mediana_sigma_y_boxplot_presentes():
     # The intro documents the three reference lines and the Tukey boxplot; the
     # per-column note carries the actual mean/median/σ numbers and the shape.
     ch = build_num_distr(_profile(n_numeric=1, extra_categorical=False), {})
-    md_texts = " ".join(b.text for b in ch.blocks if b.kind == "markdown")
+    md_texts = " ".join(b.text for b in _flatten(ch.blocks)
+                        if b.kind == "markdown")
     assert "media" in md_texts and "mediana" in md_texts
     assert "±1σ" in md_texts or "σ" in md_texts
     assert "boxplot" in md_texts.lower()
@@ -126,7 +141,8 @@ def test_anti_corte_muchas_columnas_pdf_y_pptx():
     # 8 numeric columns + long note text: nothing may be cut. Every column
     # heading must survive in both the PDF text and the PPTX deck.
     ch = build_num_distr(_profile(n_numeric=8), {})
-    names = [b.text for b in ch.blocks if b.kind == "heading" and b.level == 2]
+    names = [b.text for b in _flatten(ch.blocks)
+             if b.kind == "heading" and b.level == 2]
     assert len(names) == 8
     with tempfile.TemporaryDirectory() as d:
         pdf = os.path.join(d, "num.pdf")
@@ -143,6 +159,50 @@ def test_anti_corte_muchas_columnas_pdf_y_pptx():
         assert res_pptx["n_slides"] >= 8  # at least one slide per column figure.
 
 
+def _hist_legend_texts(numeric, box=None):
+    """Build the per-column figure and return its histogram-legend label texts."""
+    from datascience.automatic_eda.chapters.num_distr import _make_hist_box
+    import matplotlib.pyplot as plt
+    fig = _make_hist_box("col", numeric, box or {})
+    ax_h = fig.axes[0]  # the histogram is the top axis.
+    leg = ax_h.get_legend()
+    texts = [t.get_text() for t in leg.get_texts()] if leg else []
+    plt.close(fig)
+    return texts
+
+
+def test_golden_leyenda_histograma_reporta_valor_std():
+    # The histogram legend must report the numeric value of the standard
+    # deviation σ next to mean and median.
+    numeric = _numeric_block(42.5, 40.0, 12.3, 1.0, 100.0, "right-skewed", 5)
+    texts = _hist_legend_texts(numeric)
+    joined = " ".join(texts)
+    assert any("σ =" in t for t in texts), f"σ value missing in legend: {texts}"
+    assert "12.3" in joined, f"std value 12.3 not in legend: {texts}"
+    assert any("media =" in t for t in texts)
+    assert any("mediana =" in t for t in texts)
+
+
+def test_edge_std_en_leyenda_aunque_no_haya_banda():
+    # When the ±1σ band cannot be drawn (no mean) but σ is known, the legend
+    # still surfaces the σ value via a label-only proxy handle.
+    numeric = _numeric_block(42.5, 40.0, 7.5, 1.0, 100.0, "right-skewed", 0)
+    numeric["mean"] = None  # forces the band off; σ must still appear.
+    texts = _hist_legend_texts(numeric)
+    assert any("σ = 7.5" in t for t in texts), f"σ proxy missing: {texts}"
+
+
+def test_edge_sin_std_no_revienta_la_figura():
+    # A numeric block without σ must not raise and simply omits the σ entry.
+    import matplotlib.pyplot as plt
+    numeric = _numeric_block(42.5, 40.0, 0.0, 1.0, 100.0, "discrete", 0)
+    numeric["std"] = None
+    texts = _hist_legend_texts(numeric)
+    assert not any("σ =" in t for t in texts)
+    # mean/median lines still produce their own legend entries.
+    assert any("media =" in t for t in texts)
+
+
 def test_distribution_gloss_cubre_todas_las_etiquetas():
     # Every label detect_distribution_type can emit has a Spanish gloss.
     for label in ("normal-ish", "right-skewed", "left-skewed", "heavy-tail",

python/functions/datascience/automatic_eda/chapters/overview.py

@@ -20,7 +20,7 @@ from __future__ import annotations
 
 from .. import model
 
-CHAPTER_VERSION = "1.0.0"
+CHAPTER_VERSION = "1.1.0"
 CHAPTER_ID = "overview"
 CHAPTER_TITLE = "Overview"
 
@@ -90,8 +90,14 @@ def _head_block(profile: dict, ctx: dict):
         if not cols:
             cols = list(head[0].keys())
         rows = [[model._safe_str(r.get(c)) for c in cols] for r in head[:10]]
-        return model.DataTable(header=cols, rows=rows,
+        # Honest note: how many rows are shown and, when known, out of how many
-                               note=f"primeras {len(rows)} filas")
+        # rows the dataset has (so "primeras 10 filas de 891" gives context).
+        note = f"primeras {len(rows)} filas"
+        n_rows = profile.get("n_rows")
+        if isinstance(n_rows, int) and not isinstance(n_rows, bool) \
+                and n_rows > len(rows):
+            note += f" de {n_rows:,}".replace(",", ".")
+        return model.DataTable(header=cols, rows=rows, note=note)
     return model.Note(
         "df.head no disponible: el TableProfile no incluye 'head_rows'. La fase "
         "de cálculo debe añadir profile['head_rows'] (lista de dicts fila) o "

python/functions/datascience/automatic_eda/chapters/overview_test.py

@@ -0,0 +1,187 @@
+"""Tests for the OVERVIEW chapter — DoD: golden + edges + degradation.
+
+Self-contained: builds synthetic TableProfiles (no DuckDB) so the suite is fast
+and deterministic. Verifies that ``build_overview`` renders the raw first rows
+(``df.head``) as a DataTable when ``head_rows`` is present — both when it arrives
+via ``profile['head_rows']`` (populated by ``profile_table``) and via
+``ctx['head_rows']`` (populated by ``build_eda_render_ctx``) — that the chapter
+also renders the column dictionary and the numeric describe, that the full
+document renders to PDF and PPTX showing the head values, and that a profile with
+NO head data degrades to an honest note instead of raising or inventing rows.
+"""
+
+import os
+import re
+import tempfile
+
+from pypdf import PdfReader
+from pptx import Presentation
+
+from datascience.automatic_eda.model import DataTable, Note
+from datascience.automatic_eda.chapters.overview import (
+    CHAPTER_ID, CHAPTER_VERSION, build_overview,
+)
+from datascience.render_automatic_eda_pdf import render_automatic_eda_pdf
+from datascience.render_automatic_eda_pptx import render_automatic_eda_pptx
+
+
+def _columns() -> list:
+    return [
+        {"name": "PassengerId", "inferred_type": "numeric", "null_pct": 0.0,
+         "null_count": 0, "numeric": {"mean": 2.0, "median": 2.0, "min": 1.0,
+                                      "max": 3.0, "std": 1.0}},
+        {"name": "Survived", "inferred_type": "numeric", "null_pct": 0.0,
+         "null_count": 0, "numeric": {"mean": 0.33, "median": 0.0, "min": 0.0,
+                                      "max": 1.0, "std": 0.58}},
+        {"name": "Pclass", "inferred_type": "numeric", "null_pct": 0.0,
+         "null_count": 0, "numeric": {"mean": 2.33, "median": 3.0, "min": 1.0,
+                                      "max": 3.0, "std": 1.15}},
+        {"name": "Name", "inferred_type": "categorical", "null_pct": 0.0,
+         "null_count": 0, "distinct_count": 3},
+        {"name": "Sex", "inferred_type": "categorical", "null_pct": 0.0,
+         "null_count": 0, "distinct_count": 2,
+         "categorical": {"top": [{"value": "male", "count": 2},
+                                 {"value": "female", "count": 1}]}},
+    ]
+
+
+def _head_rows() -> list:
+    return [
+        {"PassengerId": 1, "Survived": 0, "Pclass": 3,
+         "Name": "Braund Owen", "Sex": "male"},
+        {"PassengerId": 2, "Survived": 1, "Pclass": 1,
+         "Name": "Cumings Florence", "Sex": "female"},
+        {"PassengerId": 3, "Survived": 1, "Pclass": 3,
+         "Name": "Heikkinen Laina", "Sex": "female"},
+    ]
+
+
+def _profile(with_head: bool = True) -> dict:
+    prof = {
+        "table": "titanic",
+        "source": "/data/titanic.csv",
+        "profiled_at": "2026-06-30T10:00:00+00:00",
+        "n_rows": 891,
+        "n_cols": 5,
+        "quality_score": 88.0,
+        "columns": _columns(),
+    }
+    if with_head:
+        prof["head_rows"] = _head_rows()
+    return prof
+
+
+def _pdf_text(path: str) -> str:
+    txt = "".join((pg.extract_text() or "") for pg in PdfReader(path).pages)
+    return re.sub(r"\s+", " ", txt)
+
+
+def _pptx_text(path: str) -> str:
+    prs = Presentation(path)
+    parts = []
+    for sl in prs.slides:
+        for sh in sl.shapes:
+            if sh.has_text_frame:
+                parts.append(sh.text_frame.text)
+            if sh.has_table:
+                tb = sh.table
+                for r in range(len(tb.rows)):
+                    for c in range(len(tb.columns)):
+                        parts.append(tb.cell(r, c).text)
+    return re.sub(r"\s+", " ", " ".join(parts))
+
+
+def _flatten(blocks):
+    """Recursively flatten Group blocks into a flat list (none here today)."""
+    out = []
+    for b in blocks:
+        inner = getattr(b, "blocks", None)
+        if inner is not None and getattr(b, "kind", None) == "group":
+            out.extend(_flatten(inner))
+        else:
+            out.append(b)
+    return out
+
+
+def test_golden_build_overview_muestra_head_desde_profile():
+    ch = build_overview(_profile(), {})
+    assert ch is not None
+    assert ch.id == CHAPTER_ID
+    assert ch.version == CHAPTER_VERSION
+    blocks = _flatten(ch.blocks)
+    # The first DataTable is df.head: its header is the column names and the
+    # real first rows are present (not a placeholder note).
+    tables = [b for b in blocks if isinstance(b, DataTable)]
+    assert tables, "overview must emit at least the df.head DataTable"
+    head_tbl = tables[0]
+    assert head_tbl.header == ["PassengerId", "Survived", "Pclass",
+                               "Name", "Sex"]
+    assert len(head_tbl.rows) == 3
+    flat = [str(c) for row in head_tbl.rows for c in row]
+    assert "Braund Owen" in flat and "Cumings Florence" in flat
+    # Honest note carries how many rows shown out of the dataset total.
+    assert head_tbl.note is not None
+    assert "primeras 3 filas" in head_tbl.note and "891" in head_tbl.note
+    # No "df.head no disponible" placeholder when head_rows is present.
+    assert not any(isinstance(b, Note) and "no disponible" in b.text
+                   for b in blocks)
+
+
+def test_golden_head_desde_ctx_tambien_funciona():
+    # head_rows absent in profile but present in ctx (build_eda_render_ctx path).
+    prof = _profile(with_head=False)
+    ch = build_overview(prof, {"head_rows": _head_rows()})
+    assert ch is not None
+    tables = [b for b in _flatten(ch.blocks) if isinstance(b, DataTable)]
+    flat = [str(c) for row in tables[0].rows for c in row]
+    assert "Braund Owen" in flat
+
+
+def test_golden_render_pdf_muestra_head():
+    with tempfile.TemporaryDirectory() as d:
+        out = os.path.join(d, "eda.pdf")
+        res = render_automatic_eda_pdf(_profile(), out, {"title": "EDA"})
+        assert res["path"] == out and os.path.exists(out)
+        assert CHAPTER_ID in [c["id"] for c in res["chapters"]]
+        txt = _pdf_text(out)
+        assert "Braund" in txt and "male" in txt
+        assert "primeras" in txt          # head note rendered.
+        assert "df.head" in txt           # chapter heading rendered.
+        assert "no disponible" not in txt  # placeholder NOT shown.
+
+
+def test_golden_render_pptx_muestra_head():
+    with tempfile.TemporaryDirectory() as d:
+        out = os.path.join(d, "eda.pptx")
+        res = render_automatic_eda_pptx(_profile(), out, {"title": "EDA"})
+        assert res["path"] == out and os.path.exists(out)
+        assert CHAPTER_ID in [c["id"] for c in res["chapters"]]
+        txt = _pptx_text(out)
+        assert "Braund" in txt and "Cumings" in txt
+
+
+def test_edge_sin_head_rows_degrada_a_nota_honesta():
+    # No head data anywhere: chapter still builds (columns exist), shows the
+    # honest placeholder note, and never invents rows nor raises.
+    prof = _profile(with_head=False)
+    ch = build_overview(prof, {})
+    assert ch is not None
+    blocks = _flatten(ch.blocks)
+    assert any(isinstance(b, Note) and "no disponible" in b.text
+               for b in blocks)
+    # The first DataTable now is the column dictionary, not df.head rows.
+    tables = [b for b in blocks if isinstance(b, DataTable)]
+    assert all("Braund" not in str(c)
+               for tbl in tables for row in tbl.rows for c in row)
+
+
+def test_edge_none_y_vacio_no_rompen():
+    # Nothing to render at all -> None, no raise.
+    assert build_overview(None, None) is None
+    assert build_overview({}, {}) is None
+    assert build_overview({"columns": []}, {}) is None
+    # Only head_rows (no columns) still yields a chapter with the head table.
+    ch = build_overview({"columns": []}, {"head_rows": _head_rows()})
+    assert ch is not None
+    tables = [b for b in _flatten(ch.blocks) if isinstance(b, DataTable)]
+    assert tables and len(tables[0].rows) == 3

python/functions/datascience/automatic_eda/chapters/portada.py

@@ -2,8 +2,17 @@
 
 Builds the document cover from a TableProfile plus an optional ``ctx`` of
 presentation metadata. Reads everything defensively (``.get``) and degrades
-honestly: a field that is neither in the profile nor in ``ctx`` is shown as a
+honestly.
-placeholder rather than invented, leaving a hook for the LLM layer to fill it.
+
+The dataset size (N rows x M columns) is always shown big, as a heading right
+under the dataset name (kept together in a ``Group``), not buried in the
+metadata table. The Description and Granularity are resolved through a cascade
+so they are never empty: an explicit ``ctx`` value wins; otherwise the LLM block
+(``profile['llm']`` from ``eda_llm_insights``) provides ``summary`` /
+``row_meaning``; otherwise a short summary is derived from the profile itself
+(shape, column-type mix, quality score) and a "Cada fila es…" sentence from the
+key-candidate columns or the table shape. Nothing is invented: the derived
+fallbacks state that they come from the profile.
 
 Contract for chapter authors (see ``docs/capabilities/automatic_eda.md``):
     build_<id>(profile: dict, ctx: dict) -> Chapter | None
@@ -17,10 +26,15 @@ from datetime import datetime, timezone
 
 from .. import model
 
-CHAPTER_VERSION = "1.0.0"
+CHAPTER_VERSION = "1.2.0"
 CHAPTER_ID = "portada"
 CHAPTER_TITLE = "Portada"
 
+# Key under which eda_llm_insights stores its interpretive block in the profile.
+# The cover reads ``summary`` (what the table is) and ``row_meaning`` (what one
+# row represents) from it when the LLM layer ran (``run_llm``).
+_LLM_KEY = "llm"
+
 # Default human description of what the table quality score measures. Chapters
 # can override it via ctx["quality_criteria"].
 _DEFAULT_QUALITY_CRITERIA = (
@@ -67,6 +81,53 @@ def _fmt_int(v) -> str:
         return str(v)
 
 
+def _fmt_pct(value) -> str:
+    """Format a percentage that may arrive as a 0–1 fraction or a 0–100 number."""
+    if value is None:
+        return "—"
+    try:
+        v = float(value)
+    except (TypeError, ValueError):
+        return str(value)
+    if 0 < v <= 1.0:
+        v *= 100.0
+    return f"{v:.1f}%"
+
+
+def _summary_blocks(summary) -> list:
+    """Mini-summary of the rest of the analysis, shown on the cover (mejora 5).
+
+    The cover is built AFTER the body (``build_document`` passes the aggregated
+    ``ctx['document_summary']``), so it can reflect what the analysis found:
+    shape, column types, quality flags and which chapters were included. Returns
+    an empty list when there is no summary (the cover degrades to its metadata
+    table only)."""
+    if not isinstance(summary, dict) or not summary:
+        return []
+    rows = []
+    n_num = summary.get("n_numeric")
+    n_cat = summary.get("n_categorical")
+    if n_num is not None or n_cat is not None:
+        rows.append(("Columnas numéricas / categóricas",
+                     f"{_fmt_int(n_num)} / {_fmt_int(n_cat)}"))
+    if summary.get("duplicate_pct") is not None:
+        rows.append(("Filas duplicadas", _fmt_pct(summary.get("duplicate_pct"))))
+    if summary.get("null_cell_pct") is not None:
+        rows.append(("Celdas nulas", _fmt_pct(summary.get("null_cell_pct"))))
+    titles = summary.get("chapter_titles") or []
+    if titles:
+        rows.append(("Capítulos del informe", _fmt_int(len(titles))))
+
+    blocks = [model.Heading(text="Resumen del análisis", level=2)]
+    if rows:
+        blocks.append(model.KVTable(rows=rows))
+    if titles:
+        bullets = "\n".join(f"- {model._safe_str(t)}" for t in titles)
+        blocks.append(model.Markdown(
+            text="Este informe incluye los siguientes capítulos:\n" + bullets))
+    return blocks
+
+
 def _fmt_date_eu(value) -> str:
     """Format a date/ISO string as European DD/MM/AAAA HH:mm (UI convention).
 
@@ -95,6 +156,88 @@ def _fmt_date_eu(value) -> str:
         return s
 
 
+def _llm_block(profile: dict, ctx: dict) -> dict:
+    """Return the interpretive LLM block (``eda_llm_insights`` output), or {}.
+
+    It is stored under ``profile['llm']`` by ``profile_table(run_llm=True)`` and
+    may also be forwarded in ``ctx['llm']``. Read defensively: anything that is
+    not a dict degrades to an empty dict so the cover never raises.
+    """
+    block = profile.get(_LLM_KEY)
+    if not isinstance(block, dict):
+        block = ctx.get(_LLM_KEY)
+    return block if isinstance(block, dict) else {}
+
+
+def _count_column_types(profile: dict, ctx: dict):
+    """Best-effort (n_numeric, n_categorical) for the dataset.
+
+    Prefers the aggregated ``ctx['document_summary']`` (computed by the engine
+    over the whole body); falls back to counting the profile columns directly so
+    the cover still has the numbers when no summary was passed.
+    """
+    summary = ctx.get("document_summary")
+    if isinstance(summary, dict):
+        n_num = summary.get("n_numeric")
+        n_cat = summary.get("n_categorical")
+        if n_num is not None or n_cat is not None:
+            return n_num, n_cat
+    cols = profile.get("columns") or []
+    n_num = sum(1 for c in cols if isinstance(c, dict)
+                and c.get("inferred_type") == "numeric")
+    n_cat = sum(1 for c in cols if isinstance(c, dict)
+                and isinstance(c.get("categorical"), dict)
+                and c.get("categorical", {}).get("top")
+                and c.get("inferred_type") != "numeric")
+    return n_num, n_cat
+
+
+def _derive_description(profile: dict, ctx: dict) -> str:
+    """A short, honest description of the dataset from the profile.
+
+    Used only when no explicit ``ctx['description']`` and no LLM ``summary`` are
+    available. Summarizes shape, column-type mix and quality score; never empty,
+    never invents business meaning (it states the description was derived)."""
+    n_rows = profile.get("n_rows")
+    n_cols = profile.get("n_cols")
+    n_num, n_cat = _count_column_types(profile, ctx)
+    head = f"Conjunto de datos con {_fmt_int(n_rows)} filas y {_fmt_int(n_cols)} columnas"
+    type_bits = []
+    if n_num:
+        type_bits.append(f"{_fmt_int(n_num)} numéricas")
+    if n_cat:
+        type_bits.append(f"{_fmt_int(n_cat)} categóricas")
+    if type_bits:
+        head += " (" + ", ".join(type_bits) + ")"
+    parts = [head + "."]
+    score = profile.get("quality_score")
+    if score is not None:
+        parts.append(f"Calidad media estimada: {score}/100.")
+    parts.append(
+        "Resumen derivado del perfil; active la interpretación LLM (`run_llm`) "
+        "para una descripción de negocio más rica.")
+    return " ".join(parts)
+
+
+def _derive_granularity(profile: dict, dataset_name: str) -> str:
+    """A ``Cada fila es…`` granularity sentence from the profile.
+
+    Prefers the key-candidate columns (a row is identified by them); when no key
+    is detected, falls back to the table shape so the line is always meaningful
+    and starts with ``Cada fila es`` as the user requested."""
+    keys = profile.get("key_candidates") or []
+    if keys:
+        shown = ", ".join(str(k) for k in keys[:3])
+        more = "" if len(keys) <= 3 else f" (y {len(keys) - 3} más)"
+        return (f"Cada fila es un registro identificado por {shown}{more}, "
+                "candidata(s) a clave por ser únicas y sin nulos.")
+    n_rows = profile.get("n_rows")
+    tail = f" El dataset tiene {_fmt_int(n_rows)} filas en total." if n_rows else ""
+    return (f"Cada fila es un registro de «{dataset_name}». No se detectó una "
+            "columna identificadora única, así que la granularidad se infiere "
+            "de la forma de la tabla." + tail)
+
+
 def build_portada(profile: dict, ctx: dict):
     """Build the cover Chapter, or None if there is truly nothing to show."""
     profile = profile or {}
@@ -119,30 +262,38 @@ def build_portada(profile: dict, ctx: dict):
     quality_criteria = ctx.get("quality_criteria") or _DEFAULT_QUALITY_CRITERIA
     quality_value = "—" if score is None else f"{score} / 100"
 
-    # Granularity: ctx wins; else derive from key candidates; else be honest.
+    llm = _llm_block(profile, ctx)
+
+    # Granularity: explicit ctx wins; then the LLM "row_meaning"; then the key
+    # candidates; finally a shape-based fallback. Always a real "Cada fila es…".
     granularity = ctx.get("granularity")
     if not granularity:
-        keys = profile.get("key_candidates") or []
+        granularity = (llm.get("row_meaning") or "").strip() or None
-        if keys:
+    if not granularity:
-            granularity = ("Cada fila parece identificada por "
+        granularity = _derive_granularity(profile, str(dataset_name))
-                           + ", ".join(str(k) for k in keys[:3]) + ".")
-        else:
-            granularity = ("Cada fila es… (granularidad no determinada — "
-                           "pendiente de la capa de cálculo/LLM).")
 
+    # Description: explicit ctx wins; then the LLM "summary"; finally a short
+    # profile-derived summary. Never the old empty placeholder.
     description = ctx.get("description")
     if not description:
-        description = ("Descripción no provista — pendiente de la capa LLM "
+        description = (llm.get("summary") or "").strip() or None
-                       "(`run_llm`) o de `ctx['description']`.")
+    if not description:
+        description = _derive_description(profile, ctx)
 
-    blocks = [
+    # Title + dataset size shown together and BIG (Heading) at the top, kept on
+    # the same page (Group). The size is no longer buried in the metadata table.
+    cover = [
         model.Heading(text=str(dataset_name), level=1),
         model.Markdown(text="**Automatic-EDA** · informe exploratorio automático"),
+        model.Heading(text=shape, level=2),
+    ]
+
+    blocks = [
+        model.Group(blocks=cover),
         model.KVTable(rows=[
             ("Fuente", source_origin),
             ("Almacenamiento", storage),
             ("Generado", when),
-            ("Tamaño", shape),
             ("Calidad", quality_value),
             ("Criterios de calidad", quality_criteria),
         ]),
@@ -152,5 +303,8 @@ def build_portada(profile: dict, ctx: dict):
         model.Markdown(text=str(granularity)),
     ]
 
+    # Mini-summary of the rest of the analysis (built last, shown on the cover).
+    blocks.extend(_summary_blocks(ctx.get("document_summary")))
+
     return model.Chapter(id=CHAPTER_ID, title=CHAPTER_TITLE,
                          version=CHAPTER_VERSION, blocks=blocks)

python/functions/datascience/automatic_eda/chapters/portada_test.py

@@ -0,0 +1,197 @@
+"""Tests for the PORTADA (cover) chapter — DoD: golden + edges + render.
+
+Self-contained: builds synthetic TableProfiles (no DuckDB) so the suite is fast
+and deterministic. Verifies the Fase 4b improvements:
+
+1. The dataset size (N rows x M columns) is always shown BIG — as a level-2
+   heading kept together with the dataset name in a ``Group`` — and is no longer
+   a row of the metadata table.
+2. Description and Granularity are resolved through a real cascade and are never
+   the old empty placeholders: an explicit ``ctx`` value wins; otherwise the LLM
+   block (``profile['llm']``) provides ``summary`` / ``row_meaning``; otherwise a
+   short summary is derived from the profile and a "Cada fila es…" sentence from
+   the key-candidate columns or the table shape.
+3. The chapter degrades without raising on empty/None input.
+4. It renders inside the full document to both PDF and PPTX showing that content.
+"""
+
+import os
+import re
+import tempfile
+
+from pypdf import PdfReader
+from pptx import Presentation
+
+from datascience.automatic_eda.model import Group, Heading, KVTable, Markdown
+from datascience.automatic_eda.chapters.portada import (
+    CHAPTER_ID, CHAPTER_VERSION, build_portada,
+)
+from datascience.render_automatic_eda_pdf import render_automatic_eda_pdf
+from datascience.render_automatic_eda_pptx import render_automatic_eda_pptx
+
+
+def _profile(with_llm: bool = True, with_keys: bool = True) -> dict:
+    prof = {
+        "table": "titanic",
+        "source": "/data/titanic.csv",
+        "profiled_at": "2026-06-30T10:00:00+00:00",
+        "n_rows": 891,
+        "n_cols": 12,
+        "quality_score": 78.0,
+        "columns": [
+            {"name": "PassengerId", "inferred_type": "numeric",
+             "null_pct": 0.0, "numeric": {"mean": 446.0, "min": 1.0,
+                                          "max": 891.0, "std": 257.0}},
+            {"name": "Survived", "inferred_type": "numeric",
+             "null_pct": 0.0, "numeric": {"mean": 0.38, "min": 0.0,
+                                          "max": 1.0, "std": 0.49}},
+            {"name": "Sex", "inferred_type": "categorical", "null_pct": 0.0,
+             "categorical": {"top": [{"value": "male", "count": 577, "pct": 0.65},
+                                     {"value": "female", "count": 314,
+                                      "pct": 0.35}],
+                             "mode": "male", "n_distinct": 2, "entropy": 0.93}},
+        ],
+    }
+    if with_keys:
+        prof["key_candidates"] = ["PassengerId"]
+    if with_llm:
+        prof["llm"] = {
+            "summary": "Pasajeros del Titanic con su supervivencia y datos de viaje.",
+            "row_meaning": "Cada fila es un pasajero del Titanic.",
+            "dictionary": [], "pii": [], "cleaning": [], "analyses": [],
+        }
+    return prof
+
+
+def _pdf_text(path: str) -> str:
+    txt = "".join((pg.extract_text() or "") for pg in PdfReader(path).pages)
+    return re.sub(r"\s+", " ", txt)
+
+
+def _pptx_text(path: str) -> str:
+    prs = Presentation(path)
+    parts = []
+    for sl in prs.slides:
+        for sh in sl.shapes:
+            if sh.has_text_frame:
+                parts.append(sh.text_frame.text)
+            if sh.has_table:
+                tb = sh.table
+                for r in range(len(tb.rows)):
+                    for c in range(len(tb.columns)):
+                        parts.append(tb.cell(r, c).text)
+    return re.sub(r"\s+", " ", " ".join(parts))
+
+
+def _markdown_after(blocks, heading_text):
+    """Return the Markdown block that follows a Heading whose text matches."""
+    for i, b in enumerate(blocks):
+        if isinstance(b, Heading) and heading_text.lower() in b.text.lower():
+            for nb in blocks[i + 1:]:
+                if isinstance(nb, Markdown):
+                    return nb
+    return None
+
+
+def test_golden_tamano_grande_y_textos_llm():
+    ch = build_portada(_profile(), {})
+    assert ch is not None
+    assert ch.id == CHAPTER_ID
+    assert ch.version == CHAPTER_VERSION
+
+    # 1) Title + size kept together in a Group; size is a BIG level-2 heading.
+    group = next(b for b in ch.blocks if isinstance(b, Group))
+    inner = group.blocks
+    assert isinstance(inner[0], Heading) and inner[0].level == 1
+    assert inner[0].text == "titanic"
+    size_h = next(b for b in inner if isinstance(b, Heading) and b.level == 2)
+    assert "891" in size_h.text and "12" in size_h.text
+    assert "filas" in size_h.text and "columnas" in size_h.text
+
+    # 2) Size is no longer a row of the metadata table.
+    kv = next(b for b in ch.blocks if isinstance(b, KVTable))
+    labels = [r[0] for r in kv.rows]
+    assert "Tamaño" not in labels
+    assert "Fuente" in labels and "Calidad" in labels
+
+    # 3) Description and Granularity come from the LLM block.
+    desc = _markdown_after(ch.blocks, "Descripción")
+    gran = _markdown_after(ch.blocks, "Granularidad")
+    assert desc is not None and "Titanic" in desc.text
+    assert gran is not None and gran.text.startswith("Cada fila es")
+    assert "pasajero" in gran.text.lower()
+
+
+def test_fallback_sin_llm_usa_keys_y_perfil():
+    # No LLM block: description derived from the profile, granularity from keys.
+    ch = build_portada(_profile(with_llm=False, with_keys=True), {})
+    desc = _markdown_after(ch.blocks, "Descripción")
+    gran = _markdown_after(ch.blocks, "Granularidad")
+    # Description is the derived summary, never the old "pendiente" placeholder.
+    assert "pendiente" not in desc.text.lower()
+    assert "891" in desc.text and "columnas" in desc.text
+    assert "numéricas" in desc.text or "categóricas" in desc.text
+    # Granularity mentions the key candidate and starts with "Cada fila es".
+    assert gran.text.startswith("Cada fila es")
+    assert "PassengerId" in gran.text
+    assert "…" not in gran.text  # the old ellipsis placeholder is gone.
+
+
+def test_fallback_sin_llm_sin_keys_usa_forma():
+    ch = build_portada(_profile(with_llm=False, with_keys=False), {})
+    gran = _markdown_after(ch.blocks, "Granularidad")
+    assert gran.text.startswith("Cada fila es")
+    assert "titanic" in gran.text.lower()
+    assert "pendiente" not in gran.text.lower()
+
+
+def test_ctx_explicito_gana_sobre_llm():
+    ctx = {"description": "Descripción manual.",
+           "granularity": "Cada fila es una unidad manual."}
+    ch = build_portada(_profile(), ctx)
+    desc = _markdown_after(ch.blocks, "Descripción")
+    gran = _markdown_after(ch.blocks, "Granularidad")
+    assert desc.text == "Descripción manual."
+    assert gran.text == "Cada fila es una unidad manual."
+
+
+def test_edge_perfil_vacio_no_lanza():
+    # Empty / None never raise; the cover still shows a size and real texts.
+    for prof, ctx in (({}, {}), (None, None)):
+        ch = build_portada(prof, ctx)
+        assert ch is not None
+        group = next(b for b in ch.blocks if isinstance(b, Group))
+        size_h = next(b for b in group.blocks
+                      if isinstance(b, Heading) and b.level == 2)
+        assert "filas" in size_h.text and "columnas" in size_h.text
+        desc = _markdown_after(ch.blocks, "Descripción")
+        gran = _markdown_after(ch.blocks, "Granularidad")
+        assert desc.text and "pendiente" not in desc.text.lower()
+        assert gran.text.startswith("Cada fila es")
+
+
+def test_golden_render_pdf_muestra_portada():
+    prof = _profile()
+    with tempfile.TemporaryDirectory() as d:
+        out = os.path.join(d, "eda.pdf")
+        res = render_automatic_eda_pdf(prof, out, {"title": "EDA"})
+        assert res["path"] == out and os.path.exists(out)
+        assert CHAPTER_ID in [c["id"] for c in res["chapters"]]
+        txt = _pdf_text(out)
+        assert "titanic" in txt.lower()
+        assert "891" in txt and "filas" in txt and "columnas" in txt
+        assert "Titanic" in txt          # LLM summary in the Description.
+        assert "Cada fila es" in txt     # granularity sentence.
+
+
+def test_golden_render_pptx_muestra_portada():
+    prof = _profile()
+    with tempfile.TemporaryDirectory() as d:
+        out = os.path.join(d, "eda.pptx")
+        res = render_automatic_eda_pptx(prof, out, {"title": "EDA"})
+        assert res["path"] == out and os.path.exists(out)
+        assert CHAPTER_ID in [c["id"] for c in res["chapters"]]
+        txt = _pptx_text(out)
+        assert "titanic" in txt.lower()
+        assert "891" in txt and "columnas" in txt
+        assert "Cada fila es" in txt

python/functions/datascience/automatic_eda/chapters/relaciones.py

@@ -0,0 +1,500 @@
+"""Key-relations chapter (RELACIONES) — the keys / join structure of the data.
+
+This chapter is the *relational* section of an AutomaticEDA report. It answers a
+single question for the table (or the whole DuckDB source it lives in): **how do
+the keys relate?** It composes, without reimplementing them, the registry's
+relation primitives and degrades honestly when a layer does not apply.
+
+It renders, in order, only the layers that have something to say:
+
+1. **Declared keys** (real schema constraints) — when the DuckDB source declares
+   PRIMARY KEY / FOREIGN KEY / UNIQUE constraints, they are read verbatim via
+   ``detect_declared_keys_duckdb`` and shown as ground truth: which column is the
+   PK, which columns are FKs and the table/column they point to.
+2. **Primary-key candidates** — the ``key_candidates`` the TableProfile already
+   carries (columns whose cardinality equals the row count, with no nulls). These
+   are *candidates*: a column that could serve as the row identifier.
+3. **Foreign-key candidates** when none are declared:
+   - **Inter-table** (the DuckDB source has several tables): real FK candidates by
+     name signal + value containment via ``infer_fk_containment_duckdb``, plus the
+     join graph (roles + a pasteable Mermaid diagram) via ``build_join_graph``.
+   - **Intra-table** (a single table): columns that *look* like a foreign key by a
+     name+cardinality heuristic (``suggest_intratable_fk_candidates``). This is a
+     **suggestion**, explicitly flagged as a heuristic, never an assertion.
+
+``build_relaciones(profile, ctx) -> Chapter | None``: returns ``None`` when there
+is nothing to say (no declared key, no key candidates, and no FK candidate —
+inter- or intra-table). Reads everything defensively (``.get``) and never raises:
+anything missing degrades to a note or is omitted; a failing registry call drops
+its layer instead of aborting the chapter.
+
+ctx keys this chapter consumes (all optional):
+    db_path, table : str — the DuckDB file and table being profiled (set by
+        ``build_eda_render_ctx``). ``db_path`` is needed to read declared
+        constraints, to list the sibling tables, and to run the containment-based
+        FK inference. Without it, only the profile-derived layers (PK candidates,
+        intra-table FK heuristic) are available.
+    glossary : model.GlossaryCollector — shared glossary; the chapter registers
+        the relational terms (PK, FK, containment, cardinality) and marks their
+        first appearance clickable.
+
+Contract: build_<id>(profile, ctx) -> Chapter | None ; CHAPTER_VERSION = "x.y.z".
+"""
+
+from __future__ import annotations
+
+from .. import model
+
+# Pure/impure registry functions (group ``eda``) this chapter composes. Imported
+# defensively (module-leaf imports, like the AGREGACION chapter) so the chapter
+# still builds — degrading the affected layer to nothing — if a function is
+# somehow unavailable / not indexed yet.
+try:
+    from datascience.detect_declared_keys_duckdb import detect_declared_keys_duckdb
+except Exception:  # noqa: BLE001 — keep the chapter importable no matter what.
+    detect_declared_keys_duckdb = None  # type: ignore[assignment]
+try:
+    from datascience.infer_fk_containment_duckdb import infer_fk_containment_duckdb
+except Exception:  # noqa: BLE001
+    infer_fk_containment_duckdb = None  # type: ignore[assignment]
+try:
+    from datascience.build_join_graph import build_join_graph
+except Exception:  # noqa: BLE001
+    build_join_graph = None  # type: ignore[assignment]
+try:
+    from datascience.suggest_intratable_fk_candidates import (
+        suggest_intratable_fk_candidates,
+    )
+except Exception:  # noqa: BLE001
+    suggest_intratable_fk_candidates = None  # type: ignore[assignment]
+try:
+    from infra import duckdb_list_tables
+except Exception:  # noqa: BLE001
+    duckdb_list_tables = None  # type: ignore[assignment]
+
+CHAPTER_VERSION = "1.0.0"
+CHAPTER_ID = "relaciones"
+CHAPTER_TITLE = "Relaciones de clave"
+
+# Cap the inter-table FK table so a wide schema does not blow up the page; the
+# rest is summarized in a closing note (no silent truncation).
+MAX_FK_ROWS = 40
+
+# --------------------------------------------------------------------------- #
+# Glossary terms this chapter explains. Registered in the shared collector and
+# marked clickable on their first appearance (contract §11.1).
+# --------------------------------------------------------------------------- #
+_TERMS = {
+    "pk": (
+        "Clave primaria (PK)",
+        "Columna (o conjunto de columnas) que identifica de forma única cada fila "
+        "de una tabla: sus valores no se repiten y no son nulos. Una tabla tiene "
+        "como mucho una clave primaria; es el ancla por la que otras tablas la "
+        "referencian.",
+    ),
+    "fk": (
+        "Clave foránea (FK)",
+        "Columna de una tabla cuyos valores apuntan a la clave primaria de otra "
+        "tabla (o de la misma), creando una relación entre ambas. Una FK suele ser "
+        "N:1: muchas filas de la tabla origen comparten el mismo valor de la tabla "
+        "destino.",
+    ),
+    "containment": (
+        "Containment / inclusión",
+        "Señal con la que se infiere una clave foránea sin que la base la declare: "
+        "la fracción de valores distintos de una columna A que también aparecen "
+        "como valores de otra columna B. Si casi todos los valores de A están "
+        "contenidos en B (inclusión ≈ 1) y B parece una clave, A → B es una FK "
+        "candidata.",
+    ),
+    "cardinalidad": (
+        "Cardinalidad",
+        "Número de valores distintos de una columna. Cardinalidad igual al número "
+        "de filas (y sin nulos) señala un identificador (candidato a clave "
+        "primaria); cardinalidad alta pero menor que el número de filas, con "
+        "valores repetidos, es típica de una clave foránea.",
+    ),
+}
+
+
+def _register_terms(ctx: dict) -> bool:
+    """Register the relational terms in the shared glossary. Returns whether the
+    in-text appearances should be marked clickable."""
+    glossary = ctx.get("glossary")
+    if not isinstance(glossary, model.GlossaryCollector):
+        return False
+    for key, (label, definition) in _TERMS.items():
+        glossary.add(key, label, definition)
+    return True
+
+
+# --------------------------------------------------------------------------- #
+# Formatting helpers (mirror the other chapters' defensive style).
+# --------------------------------------------------------------------------- #
+def _fmt_int(value) -> str:
+    if value is None:
+        return "—"
+    try:
+        return f"{int(value):,}".replace(",", ".")
+    except (TypeError, ValueError):
+        return model._safe_str(value)
+
+
+def _fmt_pct_fraction(value, decimals: int = 1) -> str:
+    """Format a 0–1 fraction as a percentage. None -> placeholder."""
+    if value is None:
+        return "—"
+    try:
+        v = float(value)
+    except (TypeError, ValueError):
+        return model._safe_str(value)
+    if v <= 1.0:
+        v *= 100.0
+    return f"{v:.{decimals}f}%"
+
+
+def _fmt_ratio(value, decimals: int = 3) -> str:
+    """Format an already-0–1 ratio (inclusion) as a plain number."""
+    if value is None:
+        return "—"
+    try:
+        return f"{float(value):.{decimals}f}".rstrip("0").rstrip(".")
+    except (TypeError, ValueError):
+        return model._safe_str(value)
+
+
+def _is_dict(v) -> bool:
+    return isinstance(v, dict)
+
+
+def _columns_by_name(profile: dict) -> dict:
+    """Index the profile columns by name for quick metric lookup."""
+    out = {}
+    for col in (profile.get("columns") or []):
+        if _is_dict(col) and col.get("name") is not None:
+            out[col.get("name")] = col
+    return out
+
+
+# --------------------------------------------------------------------------- #
+# Layer 1 — declared keys (real schema constraints).
+# --------------------------------------------------------------------------- #
+def _declared_keys(db_path: str, table: str):
+    """Read declared PK/FK/UNIQUE for the source, or None if unavailable."""
+    if not db_path or detect_declared_keys_duckdb is None:
+        return None
+    try:
+        out = detect_declared_keys_duckdb(db_path, table)
+    except Exception:  # noqa: BLE001 — dict-no-throw: treat as unavailable.
+        return None
+    if not _is_dict(out) or out.get("status") != "ok":
+        return None
+    return out
+
+
+def _declared_section(declared: dict) -> list:
+    """Blocks for the declared-keys layer, or [] if there is nothing declared."""
+    pks = [p for p in (declared.get("primary_keys") or []) if _is_dict(p)]
+    fks = [f for f in (declared.get("foreign_keys") or []) if _is_dict(f)]
+    uqs = [u for u in (declared.get("unique") or []) if _is_dict(u)]
+    if not (pks or fks or uqs):
+        return []
+
+    blocks = [
+        model.Heading(text="Claves declaradas en el esquema", level=2),
+        model.Markdown(text=(
+            "La base **declara** estas relaciones de clave como restricciones "
+            "reales del esquema (constraints). Son la verdad de referencia: no se "
+            "infieren, se leen tal cual de la definición de las tablas.")),
+    ]
+
+    if pks:
+        rows = [[model._safe_str(p.get("table")),
+                 ", ".join(model._safe_str(c) for c in (p.get("columns") or []))]
+                for p in pks]
+        blocks.append(model.DataTable(
+            header=["Tabla", "Columna(s) PK"], rows=rows,
+            title="Claves primarias declaradas",
+            note="Cada fila: la clave primaria declarada de una tabla."))
+
+    if fks:
+        rows = []
+        for f in fks:
+            src = ", ".join(model._safe_str(c) for c in (f.get("columns") or []))
+            dst = ", ".join(
+                model._safe_str(c) for c in (f.get("referenced_columns") or []))
+            rows.append([
+                model._safe_str(f.get("table")), src,
+                model._safe_str(f.get("referenced_table")), dst])
+        blocks.append(model.DataTable(
+            header=["Tabla origen", "Columna(s) FK", "→ Tabla destino",
+                    "Columna(s) destino"],
+            rows=rows, title="Claves foráneas declaradas",
+            note="Cada fila: una FK declarada — origen → destino."))
+
+    if uqs:
+        rows = [[model._safe_str(u.get("table")),
+                 ", ".join(model._safe_str(c) for c in (u.get("columns") or []))]
+                for u in uqs]
+        blocks.append(model.DataTable(
+            header=["Tabla", "Columna(s) UNIQUE"], rows=rows,
+            title="Restricciones UNIQUE declaradas"))
+
+    return blocks
+
+
+# --------------------------------------------------------------------------- #
+# Layer 2 — primary-key candidates (from the profile).
+# --------------------------------------------------------------------------- #
+def _pk_candidates_section(profile: dict, mark: bool) -> list:
+    """Blocks for the PK-candidates layer, or [] if there are none."""
+    keys = [k for k in (profile.get("key_candidates") or []) if k is not None]
+    if not keys:
+        return []
+    by_name = _columns_by_name(profile)
+
+    pk = ("[[term:pk]]**clave primaria**[[/term]]" if mark
+          else "**clave primaria**")
+    intro = (
+        f"Estas columnas son **candidatas a {pk}**: su "
+        "[[term:cardinalidad]]cardinalidad[[/term]] iguala al número de filas y no "
+        "tienen nulos, así que cada valor identifica una fila distinta. Son "
+        "candidatas, no una clave declarada: la base no las marca como tal."
+        if mark else
+        "Estas columnas son **candidatas a clave primaria**: su cardinalidad "
+        "iguala al número de filas y no tienen nulos, así que cada valor "
+        "identifica una fila distinta.")
+
+    rows = []
+    for name in keys:
+        col = by_name.get(name) or {}
+        rows.append([
+            model._safe_str(name),
+            _fmt_int(col.get("distinct_count")),
+            _fmt_pct_fraction(col.get("unique_pct")),
+            model._safe_str(col.get("inferred_type") or col.get("physical_type") or "—"),
+        ])
+    return [
+        model.Heading(text="Candidatos a clave primaria", level=2),
+        model.Markdown(text=intro),
+        model.DataTable(
+            header=["Columna", "Valores distintos", "% único", "Tipo"],
+            rows=rows, title="Candidatas a clave primaria",
+            note=f"{_fmt_int(profile.get('n_rows'))} filas en total como referencia."),
+    ]
+
+
+# --------------------------------------------------------------------------- #
+# Layer 3a — inter-table FK candidates (containment) + join graph.
+# --------------------------------------------------------------------------- #
+def _list_source_tables(db_path: str) -> list:
+    """List the tables in the DuckDB source, or [] if it can't be listed."""
+    if not db_path or duckdb_list_tables is None:
+        return []
+    try:
+        out = duckdb_list_tables(db_path)
+    except Exception:  # noqa: BLE001
+        return []
+    if not _is_dict(out) or out.get("status") != "ok":
+        return []
+    return [t for t in (out.get("tables") or []) if isinstance(t, str)]
+
+
+def _inter_table_section(db_path: str, tables: list, mark: bool) -> list:
+    """Blocks for the inter-table FK layer (containment + join graph), or []."""
+    if infer_fk_containment_duckdb is None or len(tables) < 2:
+        return []
+    try:
+        fk = infer_fk_containment_duckdb(db_path, tables=tables)
+    except Exception:  # noqa: BLE001
+        return []
+    if not _is_dict(fk) or fk.get("status") != "ok":
+        return []
+    candidates = [c for c in (fk.get("fk_candidates") or []) if _is_dict(c)]
+    if not candidates:
+        return []
+
+    containment = ("[[term:containment]]containment (inclusión de valores)[[/term]]"
+                   if mark else "containment (inclusión de valores)")
+    fk_term = "[[term:fk]]**claves foráneas**[[/term]]" if mark else "**claves foráneas**"
+    blocks = [
+        model.Heading(text="Claves foráneas candidatas (inter-tabla)", level=2),
+        model.Markdown(text=(
+            f"La fuente tiene varias tablas. Estas {fk_term} candidatas se infieren "
+            f"por señal de nombre y por {containment}: una columna de una tabla cuyos "
+            "valores están contenidos en la clave de otra. No están declaradas por "
+            "la base; son la relación más probable según los datos.")),
+    ]
+
+    shown = candidates[:MAX_FK_ROWS]
+    rows = []
+    for c in shown:
+        rows.append([
+            f"{model._safe_str(c.get('from_table'))}.{model._safe_str(c.get('from_col'))}",
+            f"{model._safe_str(c.get('to_table'))}.{model._safe_str(c.get('to_col'))}",
+            _fmt_ratio(c.get("inclusion")),
+            model._safe_str(c.get("cardinality") or "—"),
+            "sí" if c.get("name_match") else "no",
+        ])
+    note = "Ordenadas por señal de nombre e inclusión."
+    if len(candidates) > len(shown):
+        note += f" Se muestran {len(shown)} de {len(candidates)} candidatas."
+    blocks.append(model.DataTable(
+        header=["Origen", "→ Destino", "Inclusión", "Cardinalidad", "Coincide nombre"],
+        rows=rows, title="FK candidatas por containment", note=note))
+
+    # Join graph: node roles + a pasteable Mermaid diagram, kept together.
+    if build_join_graph is not None:
+        try:
+            graph = build_join_graph(candidates, tables=tables)
+        except Exception:  # noqa: BLE001
+            graph = None
+        if _is_dict(graph):
+            graph_blocks = [model.Heading(text="Grafo de relaciones", level=3)]
+            nodes = [n for n in (graph.get("nodes") or []) if _is_dict(n)]
+            if nodes:
+                node_rows = [[
+                    model._safe_str(n.get("table")),
+                    model._safe_str(n.get("role") or "—"),
+                    _fmt_int(n.get("out_degree")),
+                    _fmt_int(n.get("in_degree")),
+                ] for n in nodes]
+                graph_blocks.append(model.DataTable(
+                    header=["Tabla", "Rol", "FK salientes", "FK entrantes"],
+                    rows=node_rows, title="Tablas y su rol en el grafo",
+                    note="Rol: fact (apunta a otras), dimension (referenciada), "
+                         "bridge (ambas), standalone (aislada)."))
+            hubs = [h for h in (graph.get("hubs") or []) if h]
+            if hubs:
+                graph_blocks.append(model.Markdown(text=(
+                    "Tablas con más relaciones salientes (candidatas a tabla de "
+                    "hechos): " + ", ".join(model._safe_str(h) for h in hubs) + ".")))
+            mermaid = model._safe_str(graph.get("mermaid")).strip()
+            if mermaid:
+                graph_blocks.append(model.Markdown(text=(
+                    "Diagrama de las relaciones (pegable en un bloque Mermaid):")))
+                graph_blocks.append(model.Markdown(
+                    text="```mermaid\n" + mermaid + "\n```"))
+            if len(graph_blocks) > 1:
+                blocks.append(model.Group(blocks=graph_blocks,
+                                          title="Grafo de relaciones"))
+
+    skipped = [s for s in (fk.get("skipped") or []) if s]
+    if skipped:
+        blocks.append(model.Note(
+            "Algunos pares se omitieron por tamaño: "
+            + "; ".join(model._safe_str(s) for s in skipped) + "."))
+    return blocks
+
+
+# --------------------------------------------------------------------------- #
+# Layer 3b — intra-table FK candidates (name+cardinality heuristic).
+# --------------------------------------------------------------------------- #
+def _intra_table_section(profile: dict, mark: bool) -> list:
+    """Blocks for the intra-table FK heuristic layer, or [] if no candidates."""
+    if suggest_intratable_fk_candidates is None:
+        return []
+    try:
+        cands = suggest_intratable_fk_candidates(profile)
+    except Exception:  # noqa: BLE001
+        return []
+    cands = [c for c in (cands or []) if _is_dict(c)]
+    if not cands:
+        return []
+
+    fk_term = "[[term:fk]]**claves foráneas**[[/term]]" if mark else "**claves foráneas**"
+    blocks = [
+        model.Heading(text="Posibles claves foráneas (heurística de nombre)", level=2),
+        model.Markdown(text=(
+            f"No hay otras tablas que referenciar, pero algunas columnas **parecen** "
+            f"{fk_term} por su nombre (terminan en «id») y su cardinalidad (muchos "
+            "valores repetidos, N:1). Es una **sugerencia heurística**, no una "
+            "afirmación: el nombre de la tabla destino es una conjetura y no se "
+            "comprueba inclusión de valores contra ninguna tabla real.")),
+    ]
+    rows = []
+    for c in cands:
+        rows.append([
+            model._safe_str(c.get("column")),
+            model._safe_str(c.get("ref_table_guess") or "—"),
+            _fmt_int(c.get("distinct_count")),
+            _fmt_pct_fraction(c.get("unique_pct")),
+            model._safe_str(c.get("inferred_type") or c.get("physical_type") or "—"),
+            model._safe_str(c.get("reason") or ""),
+        ])
+    blocks.append(model.DataTable(
+        header=["Columna", "Posible tabla", "Valores distintos", "% único",
+                "Tipo", "Motivo"],
+        rows=rows, title="Posibles FK por nombre y cardinalidad",
+        note="Heurística: posibles falsos positivos/negativos. No confirma containment."))
+    blocks.append(model.Note(
+        "Estas sugerencias se basan solo en el nombre y la cardinalidad. Para "
+        "confirmarlas haría falta la tabla destino y comprobar la inclusión de "
+        "valores (containment)."))
+    return blocks
+
+
+# --------------------------------------------------------------------------- #
+# Entry point.
+# --------------------------------------------------------------------------- #
+def _intro_blocks(mark: bool) -> list:
+    pk = "[[term:pk]]clave primaria[[/term]]" if mark else "clave primaria"
+    fk = "[[term:fk]]clave foránea[[/term]]" if mark else "clave foránea"
+    text = (
+        f"Este capítulo analiza las **relaciones de clave** de la tabla: qué columna "
+        f"identifica cada fila (la {pk}) y qué columnas referencian a otra tabla (las "
+        f"{fk}). Cuando la base las **declara** como restricciones del esquema, se "
+        "muestran tal cual; cuando no, se proponen las más probables a partir de los "
+        "datos —por inclusión de valores entre tablas (containment) o, en una sola "
+        "tabla, por una heurística de nombre y cardinalidad— siempre marcadas como "
+        "candidatas, nunca como hechos.")
+    return [model.Heading(text=CHAPTER_TITLE, level=1), model.Markdown(text=text)]
+
+
+def build_relaciones(profile: dict, ctx: dict):
+    """Build the RELACIONES Chapter, or None if there is nothing to say.
+
+    Args:
+        profile: the ``eda`` group TableProfile dict (may be None/empty).
+        ctx: presentation context. Consumes ``db_path`` + ``table`` (to read
+            declared constraints, list sibling tables and run the containment FK
+            inference) and ``glossary`` (to register the relational terms).
+
+    Returns:
+        A ``model.Chapter`` with the applicable relation layers; or ``None`` when
+        the dataset has no declared key, no key candidates and no FK candidate
+        (neither inter- nor intra-table).
+    """
+    if not isinstance(profile, dict):
+        profile = {}
+    ctx = ctx if isinstance(ctx, dict) else {}
+    db_path = ctx.get("db_path")
+    table = ctx.get("table")
+
+    mark = _register_terms(ctx)
+
+    # Build each layer; the chapter is the concatenation of the non-empty ones.
+    declared = _declared_keys(db_path, table)
+    declared_blocks = _declared_section(declared) if declared else []
+    declared_has_fk = bool(declared and declared.get("foreign_keys"))
+
+    pk_blocks = _pk_candidates_section(profile, mark)
+
+    tables = _list_source_tables(db_path)
+    inter_blocks = _inter_table_section(db_path, tables, mark)
+
+    # The intra-table heuristic only makes sense when no real FK is available for
+    # this table — neither declared nor inferred inter-table. Otherwise the real
+    # relations already answer the question and the heuristic is just noise.
+    if declared_has_fk or inter_blocks:
+        intra_blocks = []
+    else:
+        intra_blocks = _intra_table_section(profile, mark)
+
+    body = declared_blocks + pk_blocks + inter_blocks + intra_blocks
+    if not body:
+        return None  # chapter does not apply: nothing to say about relations.
+
+    blocks = _intro_blocks(mark) + body
+    return model.Chapter(id=CHAPTER_ID, title=CHAPTER_TITLE,
+                         version=CHAPTER_VERSION, blocks=blocks)

python/functions/datascience/automatic_eda/chapters/relaciones_test.py

@@ -0,0 +1,273 @@
+"""Tests for the RELACIONES chapter — DoD: golden(s) + edges + no-cut render.
+
+Two goldens covering the two real paths of the chapter:
+
+- **Intra-table** (a single table, no db source for relations): the chapter shows
+  the primary-key candidates from the profile and the heuristic foreign-key
+  suggestions (name + cardinality), explicitly flagged as a heuristic. Renders to
+  PDF and PPTX with nothing cut.
+- **Inter-table** (a real DuckDB file with two related tables, customers/orders,
+  with a declared FK): the chapter shows the declared keys, the containment-based
+  FK candidates and the join graph (roles + a pasteable Mermaid diagram).
+
+Edges: a profile with no key candidate and no FK-looking column returns None;
+``None`` / ``{}`` profiles do not raise. The chapter registers its glossary terms.
+
+Layers that depend on the sibling registry functions delegated alongside this
+chapter (``detect_declared_keys_duckdb``, ``suggest_intratable_fk_candidates``)
+are asserted **conditionally on the function being importable**, so the chapter's
+honest-degradation contract is what is tested, never a hard dependency on import
+timing.
+"""
+
+import os
+import tempfile
+
+import duckdb
+from pptx import Presentation
+from pypdf import PdfReader
+
+from datascience.automatic_eda.chapters.relaciones import build_relaciones
+from datascience.automatic_eda.model import Chapter, Group, GlossaryCollector
+from datascience.render_automatic_eda_pdf import render_automatic_eda_pdf
+from datascience.render_automatic_eda_pptx import render_automatic_eda_pptx
+
+# The optional sibling functions: their layers are asserted only when present.
+try:
+    from datascience.detect_declared_keys_duckdb import detect_declared_keys_duckdb
+except Exception:  # noqa: BLE001
+    detect_declared_keys_duckdb = None
+try:
+    from datascience.suggest_intratable_fk_candidates import (
+        suggest_intratable_fk_candidates,
+    )
+except Exception:  # noqa: BLE001
+    suggest_intratable_fk_candidates = None
+
+
+# --------------------------------------------------------------------------- #
+# Helpers.
+# --------------------------------------------------------------------------- #
+def _flatten(blocks) -> list:
+    """Flatten Group blocks so a test can inspect every leaf block."""
+    out = []
+    for b in blocks:
+        if isinstance(b, Group):
+            out.extend(_flatten(b.blocks))
+        else:
+            out.append(b)
+    return out
+
+
+def _text_of(chapter: Chapter) -> str:
+    """Collect all visible text of a chapter's blocks into one string."""
+    parts = []
+    for b in _flatten(chapter.blocks):
+        for attr in ("text", "title", "note"):
+            v = getattr(b, attr, None)
+            if isinstance(v, str):
+                parts.append(v)
+        header = getattr(b, "header", None)
+        if isinstance(header, list):
+            parts.extend(str(c) for c in header)
+        rows = getattr(b, "rows", None)
+        if isinstance(rows, list):
+            for r in rows:
+                if isinstance(r, (list, tuple)):
+                    parts.extend(str(c) for c in r)
+                else:
+                    parts.append(str(r))
+    return "\n".join(parts)
+
+
+def _render_both(chapter: Chapter, tag: str):
+    """Render the chapter to PDF and PPTX; return (pdf_text, n_slides)."""
+    tmp = tempfile.mkdtemp(prefix=f"relaciones_{tag}_")
+    pdf_path = os.path.join(tmp, "out.pdf")
+    pptx_path = os.path.join(tmp, "out.pptx")
+    meta = {"title": f"EDA — {tag}"}
+    render_automatic_eda_pdf([chapter], pdf_path, meta)
+    render_automatic_eda_pptx([chapter], pptx_path, meta)
+    assert os.path.exists(pdf_path) and os.path.getsize(pdf_path) > 0
+    assert os.path.exists(pptx_path) and os.path.getsize(pptx_path) > 0
+    text = "".join(p.extract_text() or "" for p in PdfReader(pdf_path).pages)
+    n_slides = len(Presentation(pptx_path).slides)
+    return text, n_slides
+
+
+# --------------------------------------------------------------------------- #
+# Fixtures.
+# --------------------------------------------------------------------------- #
+def _titanic_profile() -> dict:
+    """A single-table profile: a PK candidate + a column that looks like a FK."""
+    return {
+        "table": "titanic",
+        "source": "/data/titanic.csv",
+        "n_rows": 891,
+        "n_cols": 4,
+        "key_candidates": ["PassengerId"],
+        "columns": [
+            {"name": "PassengerId", "inferred_type": "numeric",
+             "physical_type": "BIGINT", "distinct_count": 891,
+             "unique_pct": 1.0, "flags": ["possible_id"]},
+            {"name": "ticket_id", "inferred_type": "numeric",
+             "physical_type": "BIGINT", "distinct_count": 681,
+             "unique_pct": 0.76, "flags": []},
+            {"name": "fare", "inferred_type": "numeric",
+             "physical_type": "DOUBLE", "distinct_count": 248,
+             "unique_pct": 0.28, "flags": []},
+            {"name": "sex", "inferred_type": "categorical",
+             "physical_type": "VARCHAR", "distinct_count": 2,
+             "unique_pct": 0.002, "flags": []},
+        ],
+    }
+
+
+def _make_relational_db(path: str) -> None:
+    """Create a small DuckDB with customers(id) <- orders(customer_id), real FK."""
+    con = duckdb.connect(path)
+    con.execute("CREATE TABLE customers(id INTEGER PRIMARY KEY, name TEXT)")
+    con.execute(
+        "CREATE TABLE orders(id INTEGER PRIMARY KEY, "
+        "customer_id INTEGER REFERENCES customers(id), amount DOUBLE)")
+    con.execute("INSERT INTO customers VALUES "
+                "(1,'a'),(2,'b'),(3,'c'),(4,'d'),(5,'e')")
+    con.execute("INSERT INTO orders VALUES "
+                "(1,1,10.0),(2,1,20.0),(3,2,30.0),(4,3,40.0),"
+                "(5,3,50.0),(6,4,60.0),(7,5,70.0),(8,2,80.0)")
+    con.close()
+
+
+def _orders_profile() -> dict:
+    """A profile for the `orders` table of the relational DB."""
+    return {
+        "table": "orders",
+        "source": "orders",
+        "n_rows": 8,
+        "n_cols": 3,
+        "key_candidates": ["id"],
+        "columns": [
+            {"name": "id", "inferred_type": "numeric", "physical_type": "INTEGER",
+             "distinct_count": 8, "unique_pct": 1.0, "flags": ["possible_id"]},
+            {"name": "customer_id", "inferred_type": "numeric",
+             "physical_type": "INTEGER", "distinct_count": 5, "unique_pct": 0.625,
+             "flags": []},
+            {"name": "amount", "inferred_type": "numeric", "physical_type": "DOUBLE",
+             "distinct_count": 8, "unique_pct": 1.0, "flags": []},
+        ],
+    }
+
+
+# --------------------------------------------------------------------------- #
+# Golden 1 — intra-table.
+# --------------------------------------------------------------------------- #
+def test_golden_intra_table_pk_and_fk_heuristic():
+    """Single table: PK candidate shown; FK heuristic shown (if fn available);
+    renders to PDF + PPTX with nothing cut."""
+    prof = _titanic_profile()
+    glossary = GlossaryCollector()
+    # No db_path: only the profile-derived layers apply (no declared, no inter).
+    chapter = build_relaciones(prof, {"glossary": glossary})
+
+    assert isinstance(chapter, Chapter)
+    assert chapter.id == "relaciones"
+    text = _text_of(chapter)
+
+    # PK candidate is always present (comes from the profile).
+    assert "Candidatos a clave primaria" in text
+    assert "PassengerId" in text
+
+    # Glossary terms got registered.
+    for key in ("pk", "fk", "cardinalidad"):
+        assert glossary.has(key)
+
+    # FK heuristic layer: present iff the delegated function is importable.
+    if suggest_intratable_fk_candidates is not None:
+        assert "Posibles claves foráneas" in text
+        assert "ticket_id" in text
+        # The float measure and the PK itself are NOT suggested as FKs.
+        assert "Posibles FK por nombre" in text
+
+    pdf_text, n_slides = _render_both(chapter, "intra")
+    assert "PassengerId" in pdf_text
+    assert n_slides >= 1
+
+
+# --------------------------------------------------------------------------- #
+# Golden 2 — inter-table (real DuckDB).
+# --------------------------------------------------------------------------- #
+def test_golden_inter_table_containment_and_join_graph():
+    """Two related tables: declared FK (if fn available) + containment FK
+    candidate + Mermaid join graph."""
+    tmp = tempfile.mkdtemp(prefix="relaciones_db_")
+    db_path = os.path.join(tmp, "shop.duckdb")
+    _make_relational_db(db_path)
+
+    prof = _orders_profile()
+    glossary = GlossaryCollector()
+    chapter = build_relaciones(
+        prof, {"db_path": db_path, "table": "orders", "glossary": glossary})
+
+    assert isinstance(chapter, Chapter)
+    text = _text_of(chapter)
+
+    # Inter-table containment FK candidate: customer_id -> customers.id. This path
+    # uses infer_fk_containment_duckdb + build_join_graph, both already in the
+    # registry, so it must be present.
+    assert "Claves foráneas candidatas (inter-tabla)" in text
+    assert "orders.customer_id" in text
+    assert "customers.id" in text
+    # Join graph with a pasteable Mermaid diagram.
+    assert "Grafo de relaciones" in text
+    assert "mermaid" in text
+    assert "graph LR" in text
+    assert "containment" in text.lower()
+
+    # Declared-keys layer: present iff the delegated function is importable.
+    if detect_declared_keys_duckdb is not None:
+        assert "Claves declaradas en el esquema" in text
+        assert "Claves foráneas declaradas" in text
+
+    pdf_text, n_slides = _render_both(chapter, "inter")
+    assert "customer_id" in pdf_text
+    assert n_slides >= 1
+
+
+# --------------------------------------------------------------------------- #
+# Edges.
+# --------------------------------------------------------------------------- #
+def test_none_when_no_relations():
+    """No key candidates, no FK-looking columns, no db source -> None."""
+    prof = {
+        "table": "flat", "n_rows": 100, "n_cols": 2, "key_candidates": [],
+        "columns": [
+            {"name": "value", "inferred_type": "numeric", "physical_type": "DOUBLE",
+             "distinct_count": 50, "unique_pct": 0.5, "flags": []},
+            {"name": "label", "inferred_type": "categorical",
+             "physical_type": "VARCHAR", "distinct_count": 3, "unique_pct": 0.03,
+             "flags": []},
+        ],
+    }
+    assert build_relaciones(prof, {}) is None
+
+
+def test_empty_and_none_profile_do_not_raise():
+    """None / {} profile and missing ctx degrade to None without raising."""
+    assert build_relaciones(None, None) is None
+    assert build_relaciones({}, {}) is None
+    assert build_relaciones({}, {"glossary": GlossaryCollector()}) is None
+
+
+def test_pk_candidate_only_builds_chapter():
+    """A profile with only a key candidate (no FK anything, no db) still builds:
+    the relations chapter applies because there is a PK candidate to report."""
+    prof = {
+        "table": "t", "n_rows": 10, "n_cols": 1, "key_candidates": ["row_id"],
+        "columns": [
+            {"name": "row_id", "inferred_type": "numeric", "physical_type": "BIGINT",
+             "distinct_count": 10, "unique_pct": 1.0, "flags": ["possible_id"]},
+        ],
+    }
+    chapter = build_relaciones(prof, {})
+    assert isinstance(chapter, Chapter)
+    assert "Candidatos a clave primaria" in _text_of(chapter)

python/functions/datascience/automatic_eda/chapters_registry.py

@@ -26,19 +26,27 @@ from . import model
 # placeholders other agents will fill by creating chapters/<id>.py — they will
 # appear in this exact position automatically once their module exists.
 CHAPTER_ORDER = [
-    "portada",       # cover
+    "portada",       # cover — BUILT LAST, PLACED FIRST (see build_document).
     "overview",      # df.head + columns/types/nulls/examples + describe
     "analisis_llm",  # LLM interpretation — sits next to overview (user request)
     "num_distr",     # numeric distributions
     "cat_distr",     # categorical distributions
     "calidad",       # data quality
     "correlacion",   # correlations / associations
+    "relaciones",    # key relations: declared/candidate PK + FK (inter/intra-table)
     "modelos",       # cheap models (PCA/KMeans/outliers)
     "timeseries",    # time-series analysis
     "geospatial",    # geospatial
     "agregacion",    # aggregations / pivots
+    "glosario",      # glossary — ALWAYS LAST; clickable term destinations.
 ]
 
+# Chapters whose position is special-cased by build_document: portada is built
+# last (so it can summarize the rest) but placed first; glosario is built and
+# placed last (it reads the terms every other chapter registered).
+_PORTADA = "portada"
+_GLOSARIO = "glosario"
+
 
 def build_chapter(chapter_id: str, profile: dict, ctx: dict):
     """Build a single chapter by id, or None if absent/not-applicable/error.
@@ -75,15 +83,72 @@ def build_document(profile: dict, ctx: dict = None) -> list:
         list[Chapter] in canonical order, containing only the chapters that are
         implemented and applicable. Never raises.
     """
-    if profile is None:
-        profile = {}
     if not isinstance(profile, dict):
         profile = {}
-    if ctx is None:
+    # Copy ctx so the shared collector / summary we add do not leak to the caller.
-        ctx = {}
+    ctx = dict(ctx) if isinstance(ctx, dict) else {}
-    chapters = []
+
+    # A single glossary collector is shared by every chapter via ctx['glossary'].
+    # Chapters call ctx['glossary'].add(key, label, definition) and mark in-text
+    # appearances with [[term:key]]…[[/term]]; the glosario chapter renders the
+    # registered terms and the renderers wire the clickable links.
+    glossary = ctx.get("glossary")
+    if not isinstance(glossary, model.GlossaryCollector):
+        glossary = model.GlossaryCollector()
+        ctx["glossary"] = glossary
+
+    # 1) Body: every chapter except portada (built last) and glosario (placed
+    # last), in canonical order. This also fills the glossary collector.
+    body = []
     for cid in CHAPTER_ORDER:
+        if cid in (_PORTADA, _GLOSARIO):
+            continue
         ch = build_chapter(cid, profile, ctx)
         if ch is not None and ch.blocks:
-            chapters.append(ch)
+            body.append(ch)
+
+    # 2) Aggregated summary of the rest, for the cover (user decision: the cover
+    # is BUILT after the body so it can reflect what the analysis found).
+    ctx["document_summary"] = _summarize_document(profile, body)
+
+    # 3) Build the cover last, place it FIRST.
+    portada = build_chapter(_PORTADA, profile, ctx)
+    # 4) Build the glossary last (reads the terms the body registered), place LAST.
+    glosario = build_chapter(_GLOSARIO, profile, ctx)
+
+    chapters = []
+    if portada is not None and portada.blocks:
+        chapters.append(portada)
+    chapters.extend(body)
+    if glosario is not None and glosario.blocks:
+        chapters.append(glosario)
     return chapters
+
+
+def _summarize_document(profile: dict, body: list) -> dict:
+    """Aggregate a tiny findings summary of the body for the cover. Never raises.
+
+    Returns a dict with dataset shape, quality, column-type counts and the list
+    of chapters actually included — enough for the cover to show a mini-summary
+    of the analysis without re-deriving anything."""
+    try:
+        cols = profile.get("columns") or []
+        n_num = sum(1 for c in cols if isinstance(c, dict)
+                    and c.get("inferred_type") == "numeric")
+        n_cat = sum(1 for c in cols if isinstance(c, dict)
+                    and isinstance(c.get("categorical"), dict)
+                    and c.get("categorical", {}).get("top")
+                    and c.get("inferred_type") != "numeric")
+        return {
+            "n_chapters": len(body),
+            "chapter_titles": [getattr(c, "title", "") for c in body],
+            "n_rows": profile.get("n_rows"),
+            "n_cols": profile.get("n_cols"),
+            "quality_score": profile.get("quality_score"),
+            "n_numeric": n_num,
+            "n_categorical": n_cat,
+            "duplicate_pct": profile.get("duplicate_pct"),
+            "null_cell_pct": profile.get("null_cell_pct"),
+        }
+    except Exception:  # noqa: BLE001 — the summary is best-effort.
+        return {"n_chapters": len(body) if isinstance(body, list) else 0}

python/functions/datascience/automatic_eda/model.py

@@ -128,6 +128,39 @@ class Note:
     kind: str = field(default="note", init=False)
 
 
+@dataclass
+class Group:
+    """A keep-together unit: its blocks render on the SAME page/slide.
+
+    Renderers measure the whole group first; if it does not fit in the remaining
+    space they move it *whole* to the next page (PDF) or slide (PPTX) before
+    drawing anything — so a heading never gets stranded apart from the figure and
+    text it introduces. If the group is taller than a full page even on its own,
+    it starts on a fresh page and flows (honest degradation, never cut). Use it to
+    bind ``Heading`` + ``Markdown`` + ``Figure`` of one idea together (see the
+    DISTR NUM / AGREGACION chapters).
+    """
+
+    blocks: list = field(default_factory=list)
+    title: Optional[str] = None
+    kind: str = field(default="group", init=False)
+
+
+@dataclass
+class GlossaryEntry:
+    """One glossary term: a clickable destination at the end of the document.
+
+    Rendered as the term ``label`` (heading) plus its ``definition`` (markdown).
+    The renderers register its page/slide position as the link target so every
+    in-text appearance of the same ``key`` becomes a real clickable jump (PDF link
+    annotation via PyMuPDF; PPTX internal slide jump)."""
+
+    key: str = ""
+    label: str = ""
+    definition: str = ""
+    kind: str = field(default="glossary_entry", init=False)
+
+
 @dataclass
 class Chapter:
     """An ordered set of blocks with an id, a title and a generation version."""
@@ -150,13 +183,17 @@ _BLOCK_BY_KIND = {
     "image": Image,
     "caption": Caption,
     "note": Note,
+    "group": Group,
+    "glossary_entry": GlossaryEntry,
 }
 
 
 def as_block(obj: Any):
     """Coerce a value into a block dataclass. Unknown values become a Note."""
     if isinstance(obj, (Heading, Markdown, KVTable, DataTable, Figure, Image,
-                        Caption, Note)):
+                        Caption, Note, Group, GlossaryEntry)):
+        if isinstance(obj, Group):
+            obj.blocks = as_blocks(obj.blocks)
         return obj
     if isinstance(obj, dict):
         kind = obj.get("kind")
@@ -189,6 +226,13 @@ def as_block(obj: Any):
                 return Caption(text=_safe_str(obj.get("text")))
             if cls is Note:
                 return Note(text=_safe_str(obj.get("text")))
+            if cls is Group:
+                return Group(blocks=as_blocks(obj.get("blocks")),
+                             title=obj.get("title"))
+            if cls is GlossaryEntry:
+                return GlossaryEntry(key=_safe_str(obj.get("key")),
+                                     label=_safe_str(obj.get("label")),
+                                     definition=_safe_str(obj.get("definition")))
         except Exception:  # noqa: BLE001 — never raise on a malformed block.
             return Note(text=_safe_str(obj))
     return Note(text=_safe_str(obj))
@@ -246,6 +290,67 @@ def _safe_str(v: Any) -> str:
         return ""
 
 
+# --------------------------------------------------------------------------- #
+# Glossary collector — chapters register the terms they use; the glosario
+# chapter renders them at the end and the renderers wire the clickable links.
+# --------------------------------------------------------------------------- #
+class GlossaryCollector:
+    """Accumulates glossary terms registered by chapters during document build.
+
+    A single instance is created by :func:`build_document` and passed to every
+    chapter via ``ctx['glossary']``. A chapter calls ``add(key, label,
+    definition)`` to declare a term it explains (e.g. ``"entropia"`` →
+    "Entropía"), and marks each in-text appearance with the inline span
+    ``[[term:key]]texto visible[[/term]]`` (see ``text_layout.parse_inline_rich``).
+    The ``glosario`` chapter reads ``terms()`` to emit one :class:`GlossaryEntry`
+    per term; the renderers turn every marked appearance into a real click that
+    jumps to that entry. First registration of a key wins (idempotent); never
+    raises."""
+
+    def __init__(self):
+        self._terms: dict = {}
+        self._order: list = []
+
+    def add(self, key: Any, label: Any = None, definition: Any = "") -> str:
+        """Register a term and return its normalized key (''. if invalid)."""
+        try:
+            k = _safe_str(key).strip()
+            if not k:
+                return ""
+            if k not in self._terms:
+                self._terms[k] = {
+                    "key": k,
+                    "label": _safe_str(label).strip() or k,
+                    "definition": _safe_str(definition),
+                }
+                self._order.append(k)
+            return k
+        except Exception:  # noqa: BLE001 — collecting a term never breaks a build.
+            return ""
+
+    def has(self, key: Any) -> bool:
+        return _safe_str(key).strip() in self._terms
+
+    def get(self, key: Any) -> Optional[dict]:
+        return self._terms.get(_safe_str(key).strip())
+
+    def terms(self, by: str = "label") -> list:
+        """Return the registered terms as dicts.
+
+        ``by='label'`` (default) sorts alphabetically by visible label;
+        ``by='order'`` keeps first-appearance order."""
+        if by == "order":
+            return [self._terms[k] for k in self._order]
+        return sorted(self._terms.values(),
+                      key=lambda t: _safe_str(t.get("label")).lower())
+
+    def __len__(self) -> int:
+        return len(self._terms)
+
+    def __bool__(self) -> bool:
+        return bool(self._terms)
+
+
 # --------------------------------------------------------------------------- #
 # Manifest — per-chapter versions and page/slide counts for tracking.
 # --------------------------------------------------------------------------- #

python/functions/datascience/automatic_eda/render_features_test.py

@@ -0,0 +1,354 @@
+"""Tests for the AutomaticEDA engine features added in phase 4a.
+
+Covers, with executable evidence, the six render-engine improvements:
+
+1. Bold no longer overlaps the following text in the PDF (real width measured).
+2. Zebra striping on data tables (PDF Rectangle fills + PPTX cell fills).
+3. Keep-together: a Group moves whole to the next page/slide (heading never gets
+   stranded from its figure).
+4. Every PPTX figure carries a visible caption/title (fallback to the heading).
+5. Cover is built last but placed first and reflects an aggregated summary.
+6. Glossary is the last chapter; the term "entropía" is a real clickable link in
+   the PDF (PyMuPDF GOTO annotation) and in the PPTX (native slide-jump run).
+
+Self-contained: synthetic profiles, no DuckDB. Heavy renderer checks (fitz/pptx)
+skip cleanly when the optional engine is missing.
+"""
+
+import os
+import sys
+
+import pytest
+
+_HERE = os.path.dirname(os.path.abspath(__file__))
+_FUNCTIONS = os.path.abspath(os.path.join(_HERE, "..", "..", ".."))  # python/functions
+if _FUNCTIONS not in sys.path:
+    sys.path.insert(0, _FUNCTIONS)
+
+import matplotlib  # noqa: E402
+
+matplotlib.use("Agg")
+import matplotlib.colors as mcolors  # noqa: E402
+import matplotlib.pyplot as plt  # noqa: E402
+from matplotlib.patches import Rectangle  # noqa: E402
+
+from datascience.automatic_eda import model  # noqa: E402
+from datascience.automatic_eda import render_pdf_impl as RP  # noqa: E402
+from datascience.automatic_eda import render_pptx_impl as RX  # noqa: E402
+from datascience.automatic_eda import build_document  # noqa: E402
+from datascience.render_automatic_eda_pdf import render_automatic_eda_pdf  # noqa: E402
+from datascience.render_automatic_eda_pptx import render_automatic_eda_pptx  # noqa: E402
+
+
+class _FakePdf:
+    """Stand-in for PdfPages so the placers can call _new_page in unit tests."""
+
+    def savefig(self, fig):  # noqa: D401
+        pass
+
+
+def _small_fig():
+    fig = plt.figure(figsize=(4.0, 1.5))
+    ax = fig.add_subplot(111)
+    ax.plot([0, 1, 2], [1, 3, 2])
+    return fig
+
+
+def _profile_with_cat_and_num():
+    """A tiny profile that triggers cat_distr (→ entropía term) and num_distr."""
+    return {
+        "table": "ventas", "n_rows": 120, "n_cols": 2, "quality_score": 91,
+        "duplicate_pct": 1.5, "null_cell_pct": 0.8,
+        "columns": [
+            {"name": "region", "inferred_type": "categorical",
+             "categorical": {
+                 "top": [{"value": "norte", "count": 50, "pct": 0.42},
+                         {"value": "sur", "count": 40, "pct": 0.33},
+                         {"value": "este", "count": 30, "pct": 0.25}],
+                 "mode": "norte", "n_distinct": 3, "entropy": 1.55,
+                 "imbalance": 0.1}},
+            {"name": "importe", "inferred_type": "numeric",
+             "numeric": {"mean": 50.0, "median": 48.0, "std": 10.0,
+                         "min": 10, "max": 99, "iqr": 15,
+                         "histogram": [{"lo": 0, "hi": 50, "count": 40},
+                                       {"lo": 50, "hi": 100, "count": 80}]}},
+        ],
+    }
+
+
+# --------------------------------------------------------------------------- #
+# 1) Bold does not overlap the following text (PDF).
+# --------------------------------------------------------------------------- #
+def test_pdf_bold_span_does_not_overlap_following_text():
+    fig = plt.figure(figsize=(RP._W, RP._H))
+    st = RP._PdfState(_FakePdf(), "t")
+    st.fig = fig
+    st.page = 1
+    # A wide bold token immediately followed by normal text on the SAME line.
+    rich = [[("PALABRAMUYANCHAENNEGRITA", True, None),
+             (" texto normal justo después", False, None)]]
+    RP._place_rich_lines(st, rich, RP._FS_BODY, RP._INK)
+
+    renderer = fig.canvas.get_renderer()
+    boxes = sorted((t.get_window_extent(renderer) for t in fig.texts),
+                   key=lambda b: b.x0)
+    assert len(boxes) == 2, "se esperaban dos spans dibujados"
+    # The bold span ends before the normal span starts (no overlap). 1px slack.
+    assert boxes[0].x1 <= boxes[1].x0 + 1.0, \
+        "la negrita se solapa con el texto siguiente"
+    plt.close(fig)
+
+
+# --------------------------------------------------------------------------- #
+# 2) Zebra striping.
+# --------------------------------------------------------------------------- #
+def _facecolor_eq(artist, hexcolor) -> bool:
+    want = mcolors.to_rgba(hexcolor)
+    got = artist.get_facecolor()
+    return all(abs(a - b) < 0.02 for a, b in zip(got[:3], want[:3]))
+
+
+def test_pdf_table_has_zebra_striping():
+    fig = plt.figure(figsize=(RP._W, RP._H))
+    st = RP._PdfState(_FakePdf(), "t")
+    st.fig = fig
+    st.page = 1
+    st.chapter = model.Chapter(id="c", title="C", version="1.0.0")
+    dt = model.DataTable(header=["A", "B"],
+                         rows=[["1", "x"], ["2", "y"], ["3", "z"], ["4", "w"]])
+    RP._place_data_table(st, dt)
+    zebra = [a for a in fig.findobj(Rectangle) if _facecolor_eq(a, RP._ZEBRA)]
+    # 4 data rows → even rows (1-based 2 and 4) shaded = 2 zebra rectangles.
+    assert len(zebra) == 2, f"esperadas 2 filas zebra, hay {len(zebra)}"
+    plt.close(fig)
+
+
+def test_pptx_table_has_zebra_striping(tmp_path):
+    pptx = pytest.importorskip("pptx")
+    from pptx import Presentation
+    from pptx.dml.color import RGBColor
+
+    doc = [model.Chapter(id="c", title="Tabla", version="1.0.0", blocks=[
+        model.DataTable(header=["A", "B"],
+                        rows=[["1", "x"], ["2", "y"], ["3", "z"], ["4", "w"]])])]
+    out = str(tmp_path / "zebra.pptx")
+    assert render_automatic_eda_pptx(doc, out, {"write_manifest": False})["path"]
+
+    prs = Presentation(out)
+    table = None
+    for slide in prs.slides:
+        for sh in slide.shapes:
+            if sh.has_table:
+                table = sh.table
+                break
+    assert table is not None, "no se encontró la tabla en el deck"
+    zebra = RGBColor(0xF6, 0xF8, 0xFA)
+    white = RGBColor(0xFF, 0xFF, 0xFF)
+    # Row 0 = header; data rows follow. Even data rows (table rows 2, 4) shaded.
+    assert table.cell(1, 0).fill.fore_color.rgb == white
+    assert table.cell(2, 0).fill.fore_color.rgb == zebra
+    assert table.cell(4, 0).fill.fore_color.rgb == zebra
+
+
+# --------------------------------------------------------------------------- #
+# 3) Keep-together (Group): heading + figure never split.
+# --------------------------------------------------------------------------- #
+def test_pdf_group_moves_whole_to_next_page_when_it_does_not_fit():
+    fig = plt.figure(figsize=(RP._W, RP._H))
+    st = RP._PdfState(_FakePdf(), "t")
+    st.fig = fig
+    st.page = 1
+    st.chapter = model.Chapter(id="c", title="C", version="1.0.0")
+    grp = model.Group(blocks=[
+        model.Heading(text="Sección con figura", level=2),
+        model.Figure(make=_small_fig, caption="cap"),
+        model.Markdown(text="Descripción breve de la figura."),
+    ])
+    # Only ~0.4in left: the group does not fit here but fits on a fresh page.
+    st.y = RP._CONTENT_BOTTOM - 0.4
+    page_before = st.page
+    RP._place_group(st, grp)
+    # Exactly one page break: the whole group (heading+figure+text) stays
+    # together on the new page — no second break inside it.
+    assert st.page == page_before + 1
+    plt.close(st.fig)
+
+
+def test_pdf_group_does_not_break_when_it_fits():
+    fig = plt.figure(figsize=(RP._W, RP._H))
+    st = RP._PdfState(_FakePdf(), "t")
+    st.fig = fig
+    st.page = 1
+    st.chapter = model.Chapter(id="c", title="C", version="1.0.0")
+    grp = model.Group(blocks=[
+        model.Heading(text="Cabe entera", level=2),
+        model.Figure(make=_small_fig, caption="cap"),
+    ])
+    st.y = RP._CONTENT_TOP  # empty page → fits, must not break.
+    page_before = st.page
+    RP._place_group(st, grp)
+    assert st.page == page_before
+    plt.close(st.fig)
+
+
+def test_pptx_group_moves_whole_to_next_slide(tmp_path):
+    pytest.importorskip("pptx")
+    from pptx import Presentation
+    from pptx.util import Inches
+
+    prs = Presentation()
+    prs.slide_width = Inches(RX._W)
+    prs.slide_height = Inches(RX._H)
+    st = RX._PptxState(prs, "t")
+    st.chapter = model.Chapter(id="c", title="C", version="1.0.0")
+    RX._new_slide(st, cont=False)
+    grp = model.Group(blocks=[
+        model.Heading(text="Sección con figura", level=2),
+        model.Figure(make=_small_fig, caption="cap"),
+        model.Markdown(text="Descripción breve."),
+    ])
+    st.y = RX._CONTENT_BOTTOM - 0.4  # does not fit here.
+    slide_before = st.slide_no
+    RX._place_group(st, grp)
+    assert st.slide_no == slide_before + 1  # one jump; group kept together.
+
+
+# --------------------------------------------------------------------------- #
+# 4) Every PPTX figure carries a visible caption/title.
+# --------------------------------------------------------------------------- #
+def test_pptx_figure_without_caption_gets_heading_title(tmp_path):
+    pytest.importorskip("pptx")
+    from pptx import Presentation
+    from pptx.enum.shapes import MSO_SHAPE_TYPE
+
+    doc = [model.Chapter(id="c", title="Cap", version="1.0.0", blocks=[
+        model.Heading(text="Mi sección gráfica", level=2),
+        model.Figure(make=_small_fig),  # NO caption provided.
+    ])]
+    out = str(tmp_path / "cap.pptx")
+    assert render_automatic_eda_pptx(doc, out, {"write_manifest": False})["path"]
+
+    prs = Presentation(out)
+    for slide in prs.slides:
+        has_pic = any(sh.shape_type == MSO_SHAPE_TYPE.PICTURE
+                      for sh in slide.shapes)
+        if not has_pic:
+            continue
+        italic = [r.text for sh in slide.shapes if sh.has_text_frame
+                  for p in sh.text_frame.paragraphs for r in p.runs
+                  if r.font.italic and r.text.strip()]
+        assert italic, "la figura no lleva caption visible en su slide"
+        assert any("Mi sección gráfica" in t for t in italic), \
+            "el caption no cayó al título de la sección"
+        return
+    pytest.fail("no se encontró ningún slide con imagen")
+
+
+def test_pptx_no_figure_slide_is_ever_untitled(tmp_path):
+    """Invariant: across many figures (incl. tall ones), NO slide with an image
+    lacks a visible caption — the caption never spills to the next slide."""
+    pytest.importorskip("pptx")
+    from pptx import Presentation
+    from pptx.enum.shapes import MSO_SHAPE_TYPE
+
+    def _tall_fig():
+        fig = plt.figure(figsize=(5.0, 4.6))  # nearly square → fills the slide.
+        fig.add_subplot(111).bar([1, 2, 3], [4, 5, 6])
+        return fig
+
+    blocks = []
+    for i in range(6):
+        blocks.append(model.Heading(text=f"Gráfico {i}", level=2))
+        blocks.append(model.Figure(
+            make=_tall_fig,
+            caption=("Una descripción de la figura deliberadamente larga para "
+                     "que el caption ocupe más de una línea al envolverse en el "
+                     f"ancho del slide — figura número {i} del bloque.")))
+    doc = [model.Chapter(id="c", title="Muchas figuras", version="1.0.0",
+                         blocks=blocks)]
+    out = str(tmp_path / "many.pptx")
+    assert render_automatic_eda_pptx(doc, out, {"write_manifest": False})["path"]
+
+    prs = Presentation(out)
+    missing = []
+    pics = 0
+    for i, slide in enumerate(prs.slides):
+        if not any(sh.shape_type == MSO_SHAPE_TYPE.PICTURE
+                   for sh in slide.shapes):
+            continue
+        pics += 1
+        italic = [r.text for sh in slide.shapes if sh.has_text_frame
+                  for p in sh.text_frame.paragraphs for r in p.runs
+                  if r.font.italic and r.text.strip()]
+        if not italic:
+            missing.append(i)
+    assert pics >= 6, f"esperadas >=6 figuras, hay {pics}"
+    assert not missing, f"slides con imagen sin caption: {missing}"
+
+
+# --------------------------------------------------------------------------- #
+# 5) Cover built last, placed first, with an aggregated summary.
+# --------------------------------------------------------------------------- #
+def test_cover_first_glossary_last_with_summary():
+    chs = build_document(_profile_with_cat_and_num(), ctx={"dataset_name": "v"})
+    ids = [c.id for c in chs]
+    assert ids[0] == "portada", f"la portada no es la primera: {ids}"
+    assert ids[-1] == "glosario", f"el glosario no es el último: {ids}"
+    cover = chs[0]
+    headings = [b.text for b in cover.blocks if b.kind == "heading"]
+    assert any("Resumen" in h for h in headings), \
+        "la portada no incluye el resumen agregado"
+    # The summary reflects the body chapters (e.g. the numeric/categorical ones).
+    cover_text = " ".join(
+        b.text for b in cover.blocks if getattr(b, "kind", "") == "markdown")
+    assert "Distribuciones" in cover_text, \
+        "el resumen de portada no menciona los capítulos del cuerpo"
+
+
+# --------------------------------------------------------------------------- #
+# 6) Glossary clickable in PDF (PyMuPDF GOTO) and PPTX (native slide jump).
+# --------------------------------------------------------------------------- #
+def test_pdf_glossary_term_is_clickable(tmp_path):
+    fitz = pytest.importorskip("fitz")
+    out = str(tmp_path / "glos.pdf")
+    res = render_automatic_eda_pdf(_profile_with_cat_and_num(), out,
+                                   {"ctx": {"dataset_name": "v"},
+                                    "write_manifest": False})
+    assert res["path"] == out and os.path.exists(out)
+
+    doc = fitz.open(out)
+    goto = [(pno, l) for pno in range(doc.page_count)
+            for l in doc[pno].get_links() if l.get("kind") == fitz.LINK_GOTO]
+    doc.close()
+    assert goto, "no hay ningún enlace interno (entropía → glosario) en el PDF"
+    # Destination must be a real page in the document (the glossary page).
+    assert all(0 <= l.get("page", -1) for _p, l in goto)
+
+
+def test_pptx_glossary_term_is_clickable(tmp_path):
+    pytest.importorskip("pptx")
+    from pptx import Presentation
+    from pptx.oxml.ns import qn
+
+    out = str(tmp_path / "glos.pptx")
+    res = render_automatic_eda_pptx(_profile_with_cat_and_num(), out,
+                                    {"ctx": {"dataset_name": "v"},
+                                     "write_manifest": False})
+    assert res["path"] == out and os.path.exists(out)
+
+    prs = Presentation(out)
+    found = False
+    for slide in prs.slides:
+        for sh in slide.shapes:
+            if not sh.has_text_frame:
+                continue
+            for p in sh.text_frame.paragraphs:
+                for r in p.runs:
+                    rpr = r._r.find(qn("a:rPr"))
+                    if rpr is None:
+                        continue
+                    hl = rpr.find(qn("a:hlinkClick"))
+                    if hl is not None and \
+                            hl.get("action") == "ppaction://hlinksldjump":
+                        found = True
+    assert found, "ningún término tiene hyperlink de salto a slide en el PPTX"

python/functions/datascience/automatic_eda/render_pdf_impl.py

@@ -60,6 +60,8 @@ _FS_BODY, _FS_CELL, _FS_NOTE = 10.5, 9.0, 9.0
 _GAP = 0.12          # vertical gap after a block, inches.
 _CELL_PAD = 0.06     # horizontal padding inside a table cell, inches.
 _ROW_VPAD = 0.05     # vertical padding inside a table row, inches.
+_ZEBRA = "#f6f8fa"   # very light grey for zebra-striped (even) table rows.
+_LINK = "#2a6f97"    # accent colour for clickable glossary terms.
 
 
 class _PdfState:
@@ -73,6 +75,11 @@ class _PdfState:
         self.page = 0                # global page counter.
         self.chapter = None          # current Chapter (for the footer).
         self.chapter_pages = 0       # pages produced for the current chapter.
+        self.last_heading = ""       # text of the most recent heading.
+        # Glossary wiring (mejora 6). Pages are 0-based; rects/points are in PDF
+        # points (1/72") with a top-left origin — same convention as PyMuPDF.
+        self.term_sources = []       # [{key, page, rect:[x0,y0,x1,y1]}]
+        self.term_dests = {}         # key -> {page, point:[x,y]}
 
 
 # --------------------------------------------------------------------------- #
@@ -121,6 +128,35 @@ def _draw_footer(st: _PdfState) -> None:
         transform=st.fig.transFigure, color=_RULE, lw=0.6))
 
 
+def _text_width_in(st: _PdfState, s: str, fs: float, bold: bool) -> float:
+    """Real rendered width (inches) of ``s`` at ``fs`` with the given weight.
+
+    Measured with the Agg renderer's own font metrics (the same TrueType the PDF
+    backend embeds), so a **bold** span advances the cursor by its ACTUAL width —
+    fixing the bug where bold text overlapped the following normal text because
+    the cursor advanced by the normal-weight average-glyph estimate. Falls back to
+    the deterministic character grid if the renderer is unavailable, so it never
+    raises.
+    """
+    if not s:
+        return 0.0
+    try:
+        from matplotlib.font_manager import FontProperties
+        renderer = st.fig.canvas.get_renderer()
+        prop = FontProperties(family="sans-serif", size=fs,
+                              weight="bold" if bold else "normal")
+        w_px, _h, _d = renderer.get_text_width_height_descent(s, prop, False)
+        return w_px / float(st.fig.dpi)
+    except Exception:  # noqa: BLE001 — fall back to the conservative grid metric.
+        return tl.avg_char_width_in(fs) * len(s)
+
+
+def _pt_rect(x0_in: float, y_top_in: float, x1_in: float,
+             y_bottom_in: float) -> list:
+    """An inches box (top-left origin) → a PDF-points rect for PyMuPDF links."""
+    return [x0_in * 72.0, y_top_in * 72.0, x1_in * 72.0, y_bottom_in * 72.0]
+
+
 def _remaining(st: _PdfState) -> float:
     return _CONTENT_BOTTOM - st.y
 
@@ -138,6 +174,7 @@ def _place_heading(st: _PdfState, block) -> None:
     level = max(1, min(3, int(getattr(block, "level", 1) or 1)))
     fs = {1: _FS_H1, 2: _FS_H2, 3: _FS_H3}[level]
     text = tl.strip_inline_md(getattr(block, "text", ""))
+    st.last_heading = text or st.last_heading
     max_chars = tl.chars_per_line(_USABLE_W, fs)
     lines = tl.wrap(text, max_chars)
     lh = tl.line_height_in(fs, leading=1.2)
@@ -171,17 +208,19 @@ def _place_text_lines(st: _PdfState, lines: list, fs: float, color: str,
 
 def _place_rich_lines(st: _PdfState, rich_lines: list, fs: float, color: str,
                       indent: float = 0.0, prefixes=None) -> None:
-    """Draw pre-wrapped lines of styled segments (bold spans rendered bold).
+    """Draw pre-wrapped lines of styled segments (bold + clickable term spans).
 
-    Each line is ``[(text, is_bold), ...]``. Segments are placed left-to-right,
+    Each line is a list of ``(text, is_bold)`` or ``(text, is_bold, term_key)``
-    advancing x by the deterministic character grid (same metric the wrapper
+    segments. Segments are placed left-to-right, advancing x by the segment's
-    used), so a bold span is rendered with ``fontweight='bold'`` without
+    REAL rendered width (measured with the renderer's font metrics for the actual
-    changing the line's measured width — the no-cut guarantee is preserved.
+    weight) — this is what stops a bold span from overlapping the following text:
+    the cursor no longer advances by the normal-weight estimate. A segment with a
+    ``term_key`` is drawn in the accent colour and its rectangle is recorded in
+    ``st.term_sources`` so it becomes a clickable jump to the glossary entry.
     ``prefixes`` is an optional ``(first_line, other_lines)`` pair (e.g. a
     bullet) drawn before the segments.
     """
     lh = tl.line_height_in(fs)
-    cw = tl.avg_char_width_in(fs)
     for idx, segs in enumerate(rich_lines):
         _ensure_space(st, lh)
         x = _ML + indent
@@ -190,14 +229,23 @@ def _place_rich_lines(st: _PdfState, rich_lines: list, fs: float, color: str,
             if prefix:
                 st.fig.text(_xf(x), _yf(st.y), prefix, fontsize=fs, color=color,
                             ha="left", va="top")
-                x += cw * len(prefix)
+                x += _text_width_in(st, prefix, fs, False)
-        for seg_text, is_bold in segs:
+        for seg in segs:
+            if len(seg) == 3:
+                seg_text, is_bold, term = seg
+            else:
+                seg_text, is_bold, term = seg[0], seg[1], None
             if seg_text == "":
                 continue
-            st.fig.text(_xf(x), _yf(st.y), seg_text, fontsize=fs, color=color,
+            w = _text_width_in(st, seg_text, fs, bool(is_bold))
-                        ha="left", va="top",
+            st.fig.text(_xf(x), _yf(st.y), seg_text, fontsize=fs,
+                        color=(_LINK if term else color), ha="left", va="top",
                         fontweight="bold" if is_bold else "normal")
-            x += cw * len(seg_text)
+            if term:
+                st.term_sources.append({
+                    "key": term, "page": st.page - 1,
+                    "rect": _pt_rect(x, st.y, x + w, st.y + lh)})
+            x += w
         st.y += lh
 
 
@@ -242,7 +290,7 @@ def _place_markdown(st: _PdfState, block) -> None:
         if stripped.startswith("- ") or stripped.startswith("* "):
             content = stripped[2:]  # keep inline markers for bold rendering.
             bullet_chars = tl.chars_per_line(_USABLE_W - 0.22, _FS_BODY)
-            rich = tl.wrap_rich(content, bullet_chars)
+            rich = tl.wrap_rich_terms(content, bullet_chars)
             _place_rich_lines(st, rich, _FS_BODY, _INK,
                               prefixes=("•  ", "   "))
             i += 1
@@ -258,7 +306,8 @@ def _place_markdown(st: _PdfState, block) -> None:
             j += 1
         text = " ".join(para)
         max_chars = tl.chars_per_line(_USABLE_W, _FS_BODY)
-        _place_rich_lines(st, tl.wrap_rich(text, max_chars), _FS_BODY, _INK)
+        _place_rich_lines(st, tl.wrap_rich_terms(text, max_chars), _FS_BODY,
+                          _INK)
         i = j
     st.y += _GAP
 
@@ -325,15 +374,18 @@ def _wrap_row(cells: list, widths: list, fs: float) -> list:
 
 
 def _draw_table_row(st: _PdfState, cells_lines: list, widths: list, fs: float,
-                    y0: float, header: bool) -> float:
+                    y0: float, header: bool, zebra: bool = False) -> float:
     lh = tl.line_height_in(fs)
     nlines = max((len(c) for c in cells_lines), default=1)
     row_h = lh * nlines + _ROW_VPAD * 2
-    if header:
+    # Background: header band, or a faint zebra fill for even data rows. Drawn
+    # below the text/rule (zorder 0) so striping never hides cell content.
+    bg = _HEAD_BG if header else (_ZEBRA if zebra else None)
+    if bg is not None:
         st.fig.add_artist(Rectangle(
             (_xf(_ML), _yf(y0 + row_h)), _xf(_ML + _USABLE_W) - _xf(_ML),
             _yf(y0) - _yf(y0 + row_h), transform=st.fig.transFigure,
-            color=_HEAD_BG, lw=0, zorder=0))
+            color=bg, lw=0, zorder=0))
     x = _ML
     for c, lines in enumerate(cells_lines):
         for k, ln in enumerate(lines):
@@ -378,14 +430,18 @@ def _place_data_table(st: _PdfState, block) -> None:
             + _ROW_VPAD * 2
     _ensure_space(st, header_h() + max(first_row_h, lh))
     draw_header()
-    for r in rows:
+    # ``data_idx`` is the LOGICAL row index (not reset across page breaks) so the
+    # zebra pattern stays coherent when a long table splits and repeats the
+    # header: even rows (1-based) are shaded → 0-based odd indices.
+    for data_idx, r in enumerate(rows):
         cells_lines = _wrap_row(r, widths, fs)
         row_h = lh * max((len(c) for c in cells_lines), default=1) \
             + _ROW_VPAD * 2
         if _remaining(st) < row_h:
             _new_page(st)
             draw_header()  # repeat header on the continuation page.
-        st.y += _draw_table_row(st, cells_lines, widths, fs, st.y, header=False)
+        st.y += _draw_table_row(st, cells_lines, widths, fs, st.y,
+                                header=False, zebra=(data_idx % 2 == 1))
     note = getattr(block, "note", None)
     if note:
         _place_text_lines(st, tl.wrap(model._safe_str(note),
@@ -414,53 +470,98 @@ def _png_from_figure(fig) -> bytes:
     return buf.read()
 
 
-def _place_image_array(st: _PdfState, arr, caption) -> None:
+def _figure_png_cached(block):
+    """Rasterize a Figure to PNG bytes ONCE and cache (bytes, aspect).
+
+    Measuring (keep-together) and drawing must agree on the REAL aspect ratio:
+    ``bbox_inches='tight'`` changes it vs ``figsize``, so we rasterize once and
+    reuse the bytes for both. Cached on the block; never raises."""
+    cached = getattr(block, "_aeda_png", None)
+    if cached is not None:
+        return cached
+    fig, owned = _resolve_figure(block)
+    data = None
+    if fig is not None:
+        try:
+            data = _png_from_figure(fig)
+        finally:
+            if owned:
+                try:
+                    plt.close(fig)
+                except Exception:  # noqa: BLE001
+                    pass
+    aspect = 0.66
+    if data is not None:
+        try:
+            arr = mpimg.imread(io.BytesIO(data))
+            aspect = (arr.shape[0] / arr.shape[1]) if arr.shape[1] else 0.66
+        except Exception:  # noqa: BLE001
+            aspect = 0.66
+    try:
+        block._aeda_png = (data, aspect)
+        return block._aeda_png
+    except Exception:  # noqa: BLE001 — block may reject attributes; degrade.
+        return (data, aspect)
+
+
+def _image_aspect(block) -> float:
+    """Real aspect (h/w) of an Image block by path, for measurement."""
+    path = getattr(block, "path", "")
+    if path and os.path.exists(path):
+        try:
+            arr = mpimg.imread(path)
+            return (arr.shape[0] / arr.shape[1]) if arr.shape[1] else 0.66
+        except Exception:  # noqa: BLE001
+            pass
+    return 0.66
+
+
+def _place_image_array(st: _PdfState, arr, caption, max_h_in=None) -> None:
     h_px, w_px = arr.shape[0], arr.shape[1]
     aspect = (h_px / w_px) if w_px else 1.0
+    # Reserve the caption's REAL (possibly multi-line) height FIRST, then scale
+    # the image to (max_h - cap_reserve) so figure + caption always fit the same
+    # page. cap_reserve adds a cushion so the caption never spills to next page.
+    cap_lines = (tl.wrap(model._safe_str(caption),
+                         tl.chars_per_line(_USABLE_W, _FS_NOTE))
+                 if caption else [])
+    cap_real = tl.line_height_in(_FS_NOTE) * len(cap_lines) if caption else 0.0
+    cap_reserve = (cap_real + 0.04 + 0.08) if caption else 0.0
     max_h = _CONTENT_BOTTOM - _CONTENT_TOP
+    # height_in hint (model.Figure/Image): cap the height so a figure in a
+    # keep-together Group shrinks to leave room for its heading and text.
+    if isinstance(max_h_in, (int, float)) and max_h_in > 0:
+        max_h = min(max_h, float(max_h_in))
+    max_img_h = max(max_h - cap_reserve, 0.6)
     target_w = _USABLE_W
     target_h = target_w * aspect
-    if target_h > max_h:
+    if target_h > max_img_h:
-        target_h = max_h
+        target_h = max_img_h
         target_w = target_h / aspect if aspect else _USABLE_W
-    cap_h = tl.line_height_in(_FS_NOTE) + 0.04 if caption else 0.0
     # Move whole image to next page if it does not fit in remaining space.
-    if _remaining(st) < target_h + cap_h:
+    if _remaining(st) < target_h + cap_reserve:
-        if (max_h) >= target_h + cap_h:
+        _new_page(st)
-            _new_page(st)
-        else:
-            # Taller than a full page even at min — already clamped to max_h.
-            _new_page(st)
     left_frac = _xf(_ML + (_USABLE_W - target_w) / 2.0)
     bottom_frac = _yf(st.y + target_h)
     ax = st.fig.add_axes([left_frac, bottom_frac, target_w / _W, target_h / _H])
     ax.imshow(arr)
     ax.axis("off")
     st.y += target_h + 0.04
-    if caption:
+    if cap_lines:
-        _place_text_lines(st, tl.wrap(model._safe_str(caption),
+        _place_text_lines(st, cap_lines, _FS_NOTE, _MUTED, style="italic")
-                          tl.chars_per_line(_USABLE_W, _FS_NOTE)),
-                          _FS_NOTE, _MUTED, style="italic")
     st.y += _GAP
 
 
 def _place_figure(st: _PdfState, block) -> None:
-    fig, owned = _resolve_figure(block)
+    png, _aspect = _figure_png_cached(block)
-    if fig is None:
+    if png is None:
         _place_text_lines(st, ["(figura no disponible)"], _FS_NOTE, _MUTED,
                           style="italic")
         st.y += _GAP
         return
-    try:
-        png = _png_from_figure(fig)
-    finally:
-        if owned:
-            try:
-                plt.close(fig)
-            except Exception:  # noqa: BLE001
-                pass
     arr = mpimg.imread(io.BytesIO(png))
-    _place_image_array(st, arr, getattr(block, "caption", None))
+    _place_image_array(st, arr, getattr(block, "caption", None),
+                       max_h_in=getattr(block, "height_in", None))
 
 
 def _place_image(st: _PdfState, block) -> None:
@@ -471,7 +572,8 @@ def _place_image(st: _PdfState, block) -> None:
         st.y += _GAP
         return
     arr = mpimg.imread(path)
-    _place_image_array(st, arr, getattr(block, "caption", None))
+    _place_image_array(st, arr, getattr(block, "caption", None),
+                       max_h_in=getattr(block, "height_in", None))
 
 
 def _place_caption(st: _PdfState, block) -> None:
@@ -488,6 +590,189 @@ def _place_note(st: _PdfState, block) -> None:
     st.y += _GAP
 
 
+# --------------------------------------------------------------------------- #
+# Block measurement (mejora 3 — keep-together). These estimate a block's height
+# WITHOUT drawing it, so a Group can decide to move whole to the next page before
+# anything is drawn. Over-estimating is safe: it only triggers an earlier page
+# break, never a content cut (the placers keep their own no-cut pagination).
+# --------------------------------------------------------------------------- #
+def _measure_heading_text(text: str, level: int) -> float:
+    level = max(1, min(3, int(level or 1)))
+    fs = {1: _FS_H1, 2: _FS_H2, 3: _FS_H3}[level]
+    lines = tl.wrap(tl.strip_inline_md(text), tl.chars_per_line(_USABLE_W, fs))
+    h = tl.line_height_in(fs, leading=1.2) * len(lines) + 0.06
+    if level == 1:
+        h += 0.10
+    return h + _GAP
+
+
+def _measure_markdown(block) -> float:
+    raw = str(getattr(block, "text", "") or "")
+    md_lines = raw.split("\n")
+    h = 0.0
+    i, n = 0, len(md_lines)
+    while i < n:
+        stripped = md_lines[i].strip()
+        if stripped.startswith("|") and stripped.endswith("|"):
+            j = i
+            while j < n and md_lines[j].strip().startswith("|") \
+                    and md_lines[j].strip().endswith("|"):
+                j += 1
+            h += (tl.line_height_in(_FS_CELL) + _ROW_VPAD * 2) * (j - i) + _GAP
+            i = j
+            continue
+        if stripped == "":
+            h += tl.line_height_in(_FS_BODY) * 0.5
+            i += 1
+            continue
+        if stripped.startswith("### "):
+            h += _measure_heading_text(stripped[4:], 3)
+            i += 1
+            continue
+        if stripped.startswith("## "):
+            h += _measure_heading_text(stripped[3:], 2)
+            i += 1
+            continue
+        if stripped.startswith("# "):
+            h += _measure_heading_text(stripped[2:], 1)
+            i += 1
+            continue
+        if stripped.startswith("- ") or stripped.startswith("* "):
+            lines = tl.wrap_rich_terms(
+                stripped[2:], tl.chars_per_line(_USABLE_W - 0.22, _FS_BODY))
+            h += tl.line_height_in(_FS_BODY) * len(lines)
+            i += 1
+            continue
+        para = [stripped]
+        j = i + 1
+        while j < n:
+            nxt = md_lines[j].strip()
+            if nxt == "" or nxt.startswith(("|", "#", "- ", "* ")):
+                break
+            para.append(nxt)
+            j += 1
+        lines = tl.wrap_rich_terms(" ".join(para),
+                                   tl.chars_per_line(_USABLE_W, _FS_BODY))
+        h += tl.line_height_in(_FS_BODY) * len(lines)
+        i = j
+    return h + _GAP
+
+
+def _measure_figure_like(block) -> float:
+    max_h = _CONTENT_BOTTOM - _CONTENT_TOP
+    hint = getattr(block, "height_in", None)
+    if isinstance(hint, (int, float)) and hint > 0:
+        target_h = min(float(hint), max_h)
+    else:
+        # Real rasterized aspect (cached) so measuring matches drawing.
+        if getattr(block, "kind", "") == "image":
+            aspect = _image_aspect(block)
+        else:
+            _data, aspect = _figure_png_cached(block)
+        target_h = min(_USABLE_W * aspect, max_h)
+    cap = getattr(block, "caption", None)
+    cap_h = tl.line_height_in(_FS_NOTE) + 0.04 if cap else 0.0
+    return target_h + 0.04 + cap_h + _GAP
+
+
+def _measure_block(st: _PdfState, block) -> float:
+    kind = getattr(block, "kind", "")
+    try:
+        if kind == "heading":
+            return _measure_heading_text(getattr(block, "text", ""),
+                                         getattr(block, "level", 1))
+        if kind == "markdown":
+            return _measure_markdown(block)
+        if kind in ("figure", "image"):
+            return _measure_figure_like(block)
+        if kind in ("caption", "note"):
+            lines = tl.wrap(getattr(block, "text", ""),
+                            tl.chars_per_line(_USABLE_W, _FS_NOTE))
+            return tl.line_height_in(_FS_NOTE) * len(lines) + _GAP
+        if kind == "kv_table":
+            rows = getattr(block, "rows", []) or []
+            return (tl.line_height_in(_FS_BODY) + _ROW_VPAD) * (len(rows) + 1) \
+                + _GAP
+        if kind == "data_table":
+            rows = getattr(block, "rows", []) or []
+            return (tl.line_height_in(_FS_CELL) + _ROW_VPAD * 2) \
+                * (len(rows) + 1) + _GAP
+        if kind == "group":
+            return sum(_measure_block(st, b)
+                       for b in (getattr(block, "blocks", []) or []))
+    except Exception:  # noqa: BLE001 — a measurement never aborts rendering.
+        pass
+    return tl.line_height_in(_FS_BODY)
+
+
+def _shrink_group_figures(st: _PdfState, blocks: list, avail_full: float) -> None:
+    """Cap each figure's height (via height_in) so the whole group fits a page.
+
+    The figure shrinks just enough to leave room for its heading, text and
+    caption — keep-together puts the chart on the SAME page as its title and
+    description instead of pushing it to the next page."""
+    fig_blocks = [b for b in blocks
+                  if getattr(b, "kind", "") in ("figure", "image")]
+    if not fig_blocks:
+        return
+    nonfig_h = sum(_measure_block(st, b) for b in blocks
+                   if getattr(b, "kind", "") not in ("figure", "image"))
+    fig_overhead = tl.line_height_in(_FS_NOTE) + 0.04 + 0.04 + _GAP
+    budget = avail_full - nonfig_h - 0.08 * len(fig_blocks)
+    if budget <= 0.8:
+        return
+    per = budget / len(fig_blocks) - fig_overhead
+    if per <= 0.6:
+        return
+    for fb in fig_blocks:
+        cur = getattr(fb, "height_in", None)
+        fb.height_in = (min(float(cur), per)
+                        if isinstance(cur, (int, float)) and cur > 0 else per)
+
+
+def _place_group(st: _PdfState, block) -> None:
+    """Render a keep-together Group: move it whole to the next page if needed."""
+    blocks = getattr(block, "blocks", []) or []
+    if not blocks:
+        return
+    avail_full = _CONTENT_BOTTOM - _CONTENT_TOP
+    _shrink_group_figures(st, blocks, avail_full)
+    total = sum(_measure_block(st, b) for b in blocks)
+    if total <= avail_full:
+        # Fits on one page: keep it together by moving whole when it won't fit.
+        if total > _remaining(st):
+            _new_page(st)
+    elif st.y > _CONTENT_TOP + 1e-6:
+        # Taller than a full page: at least start it on a fresh page, then flow.
+        _new_page(st)
+    for b in blocks:
+        placer = _PLACERS.get(getattr(b, "kind", ""), _place_note)
+        try:
+            placer(st, b)
+        except Exception:  # noqa: BLE001 — a bad block never aborts the group.
+            pass
+
+
+def _place_glossary_entry(st: _PdfState, block) -> None:
+    """Render one glossary term and register it as a clickable link target."""
+    key = getattr(block, "key", "")
+    label = getattr(block, "label", "") or key
+    definition = getattr(block, "definition", "")
+    # Reserve the term + its first definition line together, then anchor the
+    # destination at the resolved page/position before drawing.
+    _ensure_space(st, tl.line_height_in(_FS_H3, leading=1.2)
+                  + tl.line_height_in(_FS_BODY) * 2)
+    if key:
+        st.term_dests[key] = {"page": st.page - 1,
+                              "point": [_ML * 72.0, st.y * 72.0]}
+    _place_heading(st, model.Heading(text=str(label), level=3))
+    if definition:
+        _place_text_lines(st, tl.wrap(model._safe_str(definition),
+                          tl.chars_per_line(_USABLE_W, _FS_BODY)),
+                          _FS_BODY, _INK)
+    st.y += _GAP * 0.5
+
+
 _PLACERS = {
     "heading": _place_heading,
     "markdown": _place_markdown,
@@ -497,6 +782,8 @@ _PLACERS = {
     "image": _place_image,
     "caption": _place_caption,
     "note": _place_note,
+    "group": _place_group,
+    "glossary_entry": _place_glossary_entry,
 }
 
 
@@ -553,8 +840,42 @@ def render_pdf(chapters: list, out_path: str, meta: dict = None) -> dict:
         return {"path": None, "n_pages": 0, "chapters": [],
                 "note": f"fallo al escribir el PDF: {e}"}
 
+    # Mejora 6 — wire clickable glossary links now the PDF is closed on disk.
+    # PdfPages cannot emit internal hyperlinks, so we post-process with PyMuPDF
+    # (delegated registry function). Degrades silently if it is unavailable.
+    n_links = _wire_glossary_links(st, out_path, notes)
+
     note = f"{n_pages} páginas"
+    if n_links:
+        note += f" · {n_links} enlaces de glosario"
     if notes:
         note += " · " + "; ".join(notes)
     return {"path": out_path, "n_pages": n_pages, "chapters": chapters_meta,
             "note": note}
+
+
+def _wire_glossary_links(st: _PdfState, out_path: str, notes: list) -> int:
+    """Build {source rect → glossary dest} links and apply them via PyMuPDF.
+
+    Returns the number of links applied (0 if there is nothing to wire or the
+    post-processor is unavailable). Never raises."""
+    try:
+        links = []
+        for src in st.term_sources:
+            dest = st.term_dests.get(src.get("key"))
+            if not dest:
+                continue
+            links.append({
+                "src_page": src["page"], "src_rect": src["rect"],
+                "dst_page": dest["page"], "dst_point": dest["point"]})
+        if not links:
+            return 0
+        from datascience.add_pdf_internal_links import add_pdf_internal_links
+        res = add_pdf_internal_links(out_path, links)
+        if isinstance(res, dict) and res.get("status") == "ok":
+            return int(res.get("n_links") or 0)
+        if isinstance(res, dict) and res.get("error"):
+            notes.append(f"glosario sin enlaces: {res.get('error')}")
+    except Exception as e:  # noqa: BLE001 — links are best-effort.
+        notes.append(f"glosario sin enlaces: {e}")
+    return 0

python/functions/datascience/automatic_eda/render_pptx_impl.py

@@ -43,6 +43,8 @@ _ACCENT = (0x2A, 0x6F, 0x97)
 _MUTED = (0x8A, 0x8A, 0x8A)
 _HEAD_BG = (0xEE, 0xF3, 0xF6)
 _WHITE = (0xFF, 0xFF, 0xFF)
+_ZEBRA = (0xF6, 0xF8, 0xFA)   # faint grey for even (zebra) data rows.
+_LINK = (0x2A, 0x6F, 0x97)    # accent colour for clickable glossary terms.
 
 _FS_TITLE = 26
 _FS_H1, _FS_H2, _FS_H3 = 20, 16, 13
@@ -59,6 +61,10 @@ class _PptxState:
         self.chapter = None
         self.slide_no = 0
         self.chapter_slides = 0
+        self.last_heading = ""        # text of the most recent heading.
+        # Glossary wiring (mejora 6): runs to link and per-term target slide.
+        self.term_runs = []           # [(key, run)]
+        self.term_anchor_slide = {}   # key -> Slide (glossary entry)
 
 
 def _rgb(c):
@@ -155,9 +161,13 @@ def _add_rich_text(st: _PptxState, rich_lines: list, fs: float, color,
                    indent=0.0, bullet=False) -> None:
     """Add pre-wrapped lines of styled segments as one paragraph per line.
 
-    Each line is ``[(text, is_bold), ...]``; every segment becomes its own run
+    Each line is a list of ``(text, is_bold)`` or ``(text, is_bold, term_key)``
-    so ``**bold**`` spans render with native PowerPoint bold (``run.font.bold``)
+    segments; every segment becomes its own run so ``**bold**`` spans render with
-    without affecting the measured height (one paragraph per pre-wrapped line).
+    native PowerPoint bold (``run.font.bold``) without affecting the measured
+    height (one paragraph per pre-wrapped line). A segment carrying a
+    ``term_key`` is drawn in the accent colour and its run is recorded in
+    ``st.term_runs`` so it later becomes a native hyperlink jumping to the
+    glossary slide of that term.
     """
     lh = tl.line_height_in(fs)
     height = lh * len(rich_lines) + 0.05
@@ -176,14 +186,20 @@ def _add_rich_text(st: _PptxState, rich_lines: list, fs: float, color,
             r0.text = "•  "
             r0.font.size = Pt(fs)
             r0.font.color.rgb = _rgb(color)
-        for seg_text, is_bold in segs:
+        for seg in segs:
+            if len(seg) == 3:
+                seg_text, is_bold, term = seg
+            else:
+                seg_text, is_bold, term = seg[0], seg[1], None
             if seg_text == "":
                 continue
             run = p.add_run()
             run.text = seg_text
             run.font.size = Pt(fs)
             run.font.bold = bool(is_bold)
-            run.font.color.rgb = _rgb(color)
+            run.font.color.rgb = _rgb(_LINK if term else color)
+            if term:
+                st.term_runs.append((term, run, st.slide))
     st.y += height
 
 
@@ -191,6 +207,7 @@ def _place_heading(st: _PptxState, block) -> None:
     level = max(1, min(3, int(getattr(block, "level", 1) or 1)))
     fs = {1: _FS_H1, 2: _FS_H2, 3: _FS_H3}[level]
     text = tl.strip_inline_md(getattr(block, "text", ""))
+    st.last_heading = text or st.last_heading
     lines = tl.wrap(text, tl.chars_per_line(_USABLE_W, fs))
     _add_text(st, lines, fs, _INK, bold=True)
     st.y += 0.04
@@ -233,12 +250,12 @@ def _place_markdown(st: _PptxState, block) -> None:
             continue
         if stripped.startswith("- ") or stripped.startswith("* "):
             content = stripped[2:]  # keep inline markers for bold rendering.
-            rich = tl.wrap_rich(content,
+            rich = tl.wrap_rich_terms(content,
-                                tl.chars_per_line(_USABLE_W - 0.3, _FS_BODY))
+                                      tl.chars_per_line(_USABLE_W - 0.3, _FS_BODY))
             _add_rich_text(st, rich, _FS_BODY, _INK, bullet=True)
             i += 1
             continue
-        para = [stripped]  # keep inline markers; wrap_rich renders **bold**.
+        para = [stripped]  # keep inline markers; wrap_rich_terms renders **bold**.
         j = i + 1
         while j < n:
             nxt = md_lines[j].strip()
@@ -247,8 +264,8 @@ def _place_markdown(st: _PptxState, block) -> None:
             para.append(nxt)
             j += 1
         text = " ".join(para)
-        _add_rich_text(st, tl.wrap_rich(text, tl.chars_per_line(_USABLE_W, _FS_BODY)),
+        _add_rich_text(st, tl.wrap_rich_terms(
-                       _FS_BODY, _INK)
+            text, tl.chars_per_line(_USABLE_W, _FS_BODY)), _FS_BODY, _INK)
         i = j
     st.y += _GAP
 
@@ -295,7 +312,8 @@ def _row_height_in(cells, widths, fs) -> float:
     return lh * maxlines + 0.10
 
 
-def _emit_table(st: _PptxState, header, chunk, widths, fs) -> None:
+def _emit_table(st: _PptxState, header, chunk, widths, fs,
+                start_index: int = 0) -> None:
     nrows = len(chunk) + (1 if header else 0)
     ncol = len(widths)
     # Pre-measure total height to size the shape (pptx still auto-grows rows).
@@ -319,11 +337,14 @@ def _emit_table(st: _PptxState, header, chunk, widths, fs) -> None:
             cell.text = model._safe_str(header[c]) if c < len(header) else ""
             _style_cell(cell, fs, _INK, bold=True, fill=_HEAD_BG)
         ridx = 1
-    for r in chunk:
+    # Zebra striping: shade even data rows (1-based) using the GLOBAL row index
+    # (start_index offset) so the pattern stays coherent across split chunks.
+    for k, r in enumerate(chunk):
+        fill = _ZEBRA if (start_index + k) % 2 == 1 else _WHITE
         for c in range(ncol):
             cell = gtable.cell(ridx, c)
             cell.text = model._safe_str(r[c]) if c < len(r) else ""
-            _style_cell(cell, fs, _INK, bold=False, fill=_WHITE)
+            _style_cell(cell, fs, _INK, bold=False, fill=fill)
         ridx += 1
     st.y += total_h + _GAP
 
@@ -367,6 +388,7 @@ def _place_data_table(st: _PptxState, block, shaded_header=True,
         avail = _remaining(st) - header_h
         chunk = []
         used = 0.0
+        chunk_start = idx  # global index of the first row in this chunk (zebra).
         while idx < n:
             rh = _row_height_in(rows[idx], widths, fs)
             if used + rh > avail and chunk:
@@ -374,7 +396,7 @@ def _place_data_table(st: _PptxState, block, shaded_header=True,
             chunk.append(rows[idx])
             used += rh
             idx += 1
-        _emit_table(st, header, chunk, widths, fs)
+        _emit_table(st, header, chunk, widths, fs, start_index=chunk_start)
     note = getattr(block, "note", None)
     if note:
         _add_text(st, tl.wrap(model._safe_str(note),
@@ -421,54 +443,97 @@ def _resolve_png(block):
                 pass
 
 
-def _place_picture_bytes(st: _PptxState, data: bytes, caption) -> None:
+def _figure_bytes_cached(block):
+    """Rasterize a figure/image to PNG bytes ONCE and cache (bytes, aspect).
+
+    Measuring (keep-together) and drawing must agree on the real aspect ratio —
+    ``bbox_inches='tight'`` changes it vs ``figsize``, so we rasterize once and
+    reuse the bytes for both. Cached on the block; never raises."""
+    cached = getattr(block, "_aeda_png", None)
+    if cached is not None:
+        return cached
+    kind = getattr(block, "kind", "")
+    data = None
+    if kind == "image":
+        path = getattr(block, "path", "")
+        if path and os.path.exists(path):
+            try:
+                with open(path, "rb") as fh:
+                    data = fh.read()
+            except Exception:  # noqa: BLE001
+                data = None
+    else:
+        data = _resolve_png(block)
+    aspect = 0.66
+    if data is not None:
+        w_px, h_px = _img_size_px(data)
+        aspect = (h_px / w_px) if w_px else 0.66
+    try:
+        block._aeda_png = (data, aspect)
+        return block._aeda_png
+    except Exception:  # noqa: BLE001 — block may reject attributes; degrade.
+        return (data, aspect)
+
+
+def _place_picture_bytes(st: _PptxState, data: bytes, caption,
+                         max_h_in=None) -> None:
+    # Mejora 4 — every figure on a slide carries a visible caption/title. If the
+    # block has no caption, fall back to the current section heading, then to a
+    # generic label, so no image is ever shown untitled.
+    caption = (model._safe_str(caption).strip()
+               or model._safe_str(st.last_heading).strip() or "Figura")
     w_px, h_px = _img_size_px(data)
     aspect = (h_px / w_px) if w_px else 0.66
+    # Reserve the caption's REAL (possibly multi-line) height FIRST, then scale
+    # the image to (max_h - cap_reserve): a figure never fills the whole slide,
+    # so its caption always fits on the SAME slide and no image is untitled.
+    # cap_real = what _add_text consumes; cap_reserve adds the post-image gap and
+    # a small cushion so the caption never spills to the next slide.
+    cap_lines = tl.wrap(caption, tl.chars_per_line(_USABLE_W, _FS_NOTE))
+    cap_real = tl.line_height_in(_FS_NOTE) * len(cap_lines) + 0.05
+    cap_reserve = cap_real + 0.05 + 0.10
     max_h = _CONTENT_BOTTOM - _CONTENT_TOP
+    # height_in hint (model.Figure/Image): cap the target height so a figure in a
+    # keep-together Group shrinks to leave room for its heading and text.
+    if isinstance(max_h_in, (int, float)) and max_h_in > 0:
+        max_h = min(max_h, float(max_h_in))
+    max_img_h = max(max_h - cap_reserve, 0.6)
     target_w = _USABLE_W
     target_h = target_w * aspect
-    if target_h > max_h:
+    if target_h > max_img_h:
-        target_h = max_h
+        target_h = max_img_h
         target_w = target_h / aspect if aspect else _USABLE_W
-    cap_h = tl.line_height_in(_FS_NOTE) + 0.05 if caption else 0.0
+    # Keep the image and its caption together on the same slide.
-    if _remaining(st) < target_h + cap_h:
+    if _remaining(st) < target_h + cap_reserve:
         _new_slide(st, cont=True)
     left = _ML + (_USABLE_W - target_w) / 2.0
     st.slide.shapes.add_picture(io.BytesIO(data), Inches(left), Inches(st.y),
                                 width=Inches(target_w), height=Inches(target_h))
     st.y += target_h + 0.05
-    if caption:
+    _add_text(st, cap_lines, _FS_NOTE, _MUTED, italic=True)
-        _add_text(st, tl.wrap(model._safe_str(caption),
-                  tl.chars_per_line(_USABLE_W, _FS_NOTE)), _FS_NOTE, _MUTED,
-                  italic=True)
     st.y += _GAP
 
 
 def _place_figure(st: _PptxState, block) -> None:
-    png = _resolve_png(block)
+    png, _aspect = _figure_bytes_cached(block)
     if png is None:
         _add_text(st, ["(figura no disponible)"], _FS_NOTE, _MUTED, italic=True)
         st.y += _GAP
         return
-    _place_picture_bytes(st, png, getattr(block, "caption", None))
+    _place_picture_bytes(st, png, getattr(block, "caption", None),
+                         max_h_in=getattr(block, "height_in", None))
 
 
 def _place_image(st: _PptxState, block) -> None:
-    path = getattr(block, "path", "")
+    data, _aspect = _figure_bytes_cached(block)
-    if not path or not os.path.exists(path):
+    if data is None:
+        path = getattr(block, "path", "")
         _add_text(st, [f"(imagen no encontrada: {path})"], _FS_NOTE, _MUTED,
                   italic=True)
         st.y += _GAP
         return
-    try:
+    _place_picture_bytes(st, data, getattr(block, "caption", None),
-        with open(path, "rb") as fh:
+                         max_h_in=getattr(block, "height_in", None))
-            data = fh.read()
-    except Exception as e:  # noqa: BLE001
-        _add_text(st, [f"(no se pudo leer la imagen: {e})"], _FS_NOTE, _MUTED,
-                  italic=True)
-        st.y += _GAP
-        return
-    _place_picture_bytes(st, data, getattr(block, "caption", None))
 
 
 def _place_caption(st: _PptxState, block) -> None:
@@ -482,6 +547,170 @@ def _place_note(st: _PptxState, block) -> None:
     _place_caption(st, block)
 
 
+# --------------------------------------------------------------------------- #
+# Block measurement (mejora 3 — keep-together). Estimate a block's slide height
+# WITHOUT drawing it so a Group can move whole to the next slide before drawing.
+# Over-estimating only triggers an earlier slide break, never a content cut.
+# --------------------------------------------------------------------------- #
+def _measure_heading_text(text: str, level: int) -> float:
+    level = max(1, min(3, int(level or 1)))
+    fs = {1: _FS_H1, 2: _FS_H2, 3: _FS_H3}[level]
+    lines = tl.wrap(tl.strip_inline_md(text), tl.chars_per_line(_USABLE_W, fs))
+    return tl.line_height_in(fs) * len(lines) + 0.05 + 0.04
+
+
+def _measure_markdown(block) -> float:
+    raw = str(getattr(block, "text", "") or "")
+    md_lines = raw.split("\n")
+    h = 0.0
+    i, n = 0, len(md_lines)
+    while i < n:
+        stripped = md_lines[i].strip()
+        if stripped.startswith("|") and stripped.endswith("|"):
+            j = i
+            while j < n and md_lines[j].strip().startswith("|") \
+                    and md_lines[j].strip().endswith("|"):
+                j += 1
+            h += (tl.line_height_in(_FS_CELL) + 0.10) * (j - i) + _GAP
+            i = j
+            continue
+        if stripped == "":
+            h += tl.line_height_in(_FS_BODY) * 0.4
+            i += 1
+            continue
+        if stripped.startswith("### "):
+            h += _measure_heading_text(stripped[4:], 3)
+            i += 1
+            continue
+        if stripped.startswith("## "):
+            h += _measure_heading_text(stripped[3:], 2)
+            i += 1
+            continue
+        if stripped.startswith("# "):
+            h += _measure_heading_text(stripped[2:], 1)
+            i += 1
+            continue
+        if stripped.startswith("- ") or stripped.startswith("* "):
+            lines = tl.wrap_rich_terms(
+                stripped[2:], tl.chars_per_line(_USABLE_W - 0.3, _FS_BODY))
+            h += tl.line_height_in(_FS_BODY) * len(lines) + 0.05
+            i += 1
+            continue
+        para = [stripped]
+        j = i + 1
+        while j < n:
+            nxt = md_lines[j].strip()
+            if nxt == "" or nxt.startswith(("|", "#", "- ", "* ")):
+                break
+            para.append(nxt)
+            j += 1
+        lines = tl.wrap_rich_terms(" ".join(para),
+                                   tl.chars_per_line(_USABLE_W, _FS_BODY))
+        h += tl.line_height_in(_FS_BODY) * len(lines) + 0.05
+        i = j
+    return h + _GAP
+
+
+def _measure_figure_like(block) -> float:
+    max_h = _CONTENT_BOTTOM - _CONTENT_TOP
+    hint = getattr(block, "height_in", None)
+    if isinstance(hint, (int, float)) and hint > 0:
+        max_h = min(max_h, float(hint))
+    # Use the REAL rasterized aspect (cached) so measuring matches drawing — this
+    # is what keeps a figure together with its heading instead of splitting.
+    _data, aspect = _figure_bytes_cached(block)
+    target_h = min(_USABLE_W * aspect, max_h)
+    # Caption is always emitted now (mejora 4), so always reserve its line.
+    cap_h = tl.line_height_in(_FS_NOTE) + 0.05
+    return target_h + 0.05 + cap_h + _GAP
+
+
+def _measure_block(st: _PptxState, block) -> float:
+    kind = getattr(block, "kind", "")
+    try:
+        if kind == "heading":
+            return _measure_heading_text(getattr(block, "text", ""),
+                                         getattr(block, "level", 1))
+        if kind == "markdown":
+            return _measure_markdown(block)
+        if kind in ("figure", "image"):
+            return _measure_figure_like(block)
+        if kind in ("caption", "note"):
+            lines = tl.wrap(getattr(block, "text", ""),
+                            tl.chars_per_line(_USABLE_W, _FS_NOTE))
+            return tl.line_height_in(_FS_NOTE) * len(lines) + 0.05 + _GAP
+        if kind in ("kv_table", "data_table"):
+            rows = getattr(block, "rows", []) or []
+            return (tl.line_height_in(_FS_CELL) + 0.10) * (len(rows) + 1) + _GAP
+        if kind == "group":
+            return sum(_measure_block(st, b)
+                       for b in (getattr(block, "blocks", []) or []))
+    except Exception:  # noqa: BLE001 — a measurement never aborts rendering.
+        pass
+    return tl.line_height_in(_FS_BODY)
+
+
+def _shrink_group_figures(st: _PptxState, blocks: list, avail_full: float) -> None:
+    """Cap each figure's height (via height_in) so the whole group fits a slide.
+
+    The figure shrinks just enough to leave room for its heading, text and
+    caption — that is how keep-together puts a chart on the SAME slide as its
+    title and description instead of pushing it to the next slide."""
+    fig_blocks = [b for b in blocks
+                  if getattr(b, "kind", "") in ("figure", "image")]
+    if not fig_blocks:
+        return
+    nonfig_h = sum(_measure_block(st, b) for b in blocks
+                   if getattr(b, "kind", "") not in ("figure", "image"))
+    fig_overhead = tl.line_height_in(_FS_NOTE) + 0.05 + 0.05 + _GAP
+    budget = avail_full - nonfig_h - 0.10 * len(fig_blocks)
+    if budget <= 1.0:
+        return  # not enough room to keep together; let it flow (degrade).
+    per = budget / len(fig_blocks) - fig_overhead
+    if per <= 0.8:
+        return
+    for fb in fig_blocks:
+        cur = getattr(fb, "height_in", None)
+        fb.height_in = (min(float(cur), per)
+                        if isinstance(cur, (int, float)) and cur > 0 else per)
+
+
+def _place_group(st: _PptxState, block) -> None:
+    """Render a keep-together Group: move it whole to the next slide if needed."""
+    blocks = getattr(block, "blocks", []) or []
+    if not blocks:
+        return
+    avail_full = _CONTENT_BOTTOM - _CONTENT_TOP
+    _shrink_group_figures(st, blocks, avail_full)
+    total = sum(_measure_block(st, b) for b in blocks)
+    if total <= avail_full:
+        if total > _remaining(st):
+            _new_slide(st, cont=True)
+    elif st.y > _CONTENT_TOP + 1e-6:
+        _new_slide(st, cont=True)
+    for b in blocks:
+        placer = _PLACERS.get(getattr(b, "kind", ""), _place_note)
+        try:
+            placer(st, b)
+        except Exception:  # noqa: BLE001 — a bad block never aborts the group.
+            pass
+
+
+def _place_glossary_entry(st: _PptxState, block) -> None:
+    """Render one glossary term and register its slide as the link target."""
+    key = getattr(block, "key", "")
+    label = getattr(block, "label", "") or key
+    definition = getattr(block, "definition", "")
+    _ensure(st, tl.line_height_in(_FS_H3) + tl.line_height_in(_FS_BODY) * 2)
+    if key:
+        st.term_anchor_slide[key] = st.slide
+    _place_heading(st, model.Heading(text=str(label), level=3))
+    if definition:
+        _add_text(st, tl.wrap(model._safe_str(definition),
+                  tl.chars_per_line(_USABLE_W, _FS_BODY)), _FS_BODY, _INK)
+    st.y += _GAP
+
+
 _PLACERS = {
     "heading": _place_heading,
     "markdown": _place_markdown,
@@ -491,6 +720,8 @@ _PLACERS = {
     "image": _place_image,
     "caption": _place_caption,
     "note": _place_note,
+    "group": _place_group,
+    "glossary_entry": _place_glossary_entry,
 }
 
 
@@ -542,6 +773,9 @@ def render_pptx(chapters: list, out_path: str, meta: dict = None) -> dict:
             _new_slide(st, cont=False)
             _place_note(st, model.Note(
                 "(documento vacío — sin capítulos aplicables)"))
+        # Mejora 6 — wire clickable glossary terms to their entry slide (native
+        # PowerPoint slide-jump). Delegated registry function; degrades silently.
+        n_links = _wire_glossary_links(st, notes)
         prs.save(out_path)
         n_slides = st.slide_no
     except Exception as e:  # noqa: BLE001
@@ -549,7 +783,35 @@ def render_pptx(chapters: list, out_path: str, meta: dict = None) -> dict:
                 "note": f"fallo al escribir el PPTX: {e}"}
 
     note = f"{n_slides} slides"
+    if n_links:
+        note += f" · {n_links} enlaces de glosario"
     if notes:
         note += " · " + "; ".join(notes)
     return {"path": out_path, "n_slides": n_slides, "chapters": chapters_meta,
             "note": note}
+
+
+def _wire_glossary_links(st: _PptxState, notes: list) -> int:
+    """Turn each recorded term run into a native jump to its glossary slide.
+
+    Returns the number of links applied. A term whose only appearance is inside
+    its own glossary entry (source slide == target slide) is skipped. Never
+    raises."""
+    if not st.term_runs or not st.term_anchor_slide:
+        return 0
+    linked = 0
+    try:
+        from datascience.pptx_link_run_to_slide import pptx_link_run_to_slide
+    except Exception as e:  # noqa: BLE001
+        notes.append(f"glosario sin enlaces: {e}")
+        return 0
+    for key, run, src_slide in st.term_runs:
+        tgt = st.term_anchor_slide.get(key)
+        if tgt is None or tgt is src_slide:
+            continue
+        try:
+            if pptx_link_run_to_slide(run, src_slide, tgt):
+                linked += 1
+        except Exception:  # noqa: BLE001 — links are best-effort.
+            pass
+    return linked

python/functions/datascience/automatic_eda/text_layout.py

@@ -24,6 +24,13 @@ import textwrap
 # the visible text matches ``strip_inline_md`` exactly.
 _INLINE_SPAN_RE = re.compile(r"(\*\*.+?\*\*|__.+?__|`.+?`)")
 
+# Glossary term span: ``[[term:key]]texto visible[[/term]]``. The visible text
+# (which may itself contain ``**bold**``) is kept and tagged with ``key`` so the
+# renderers can turn each appearance into a clickable jump to the glossary entry.
+_TERM_SPAN_RE = re.compile(r"\[\[term:([A-Za-z0-9_]+)\]\](.*?)\[\[/term\]\]",
+                           re.S)
+_TERM_OPEN_RE = re.compile(r"\[\[term:[A-Za-z0-9_]+\]\]")
+
 
 def avg_char_width_in(fontsize_pt: float) -> float:
     """Approximate average glyph width in inches for a sans-serif font.
@@ -86,11 +93,21 @@ def strip_inline_md(text: str) -> str:
     if not text:
         return ""
     s = str(text)
+    # Drop glossary term markers, keeping the visible inner text.
+    s = _TERM_SPAN_RE.sub(lambda m: m.group(2), s)
+    s = _TERM_OPEN_RE.sub("", s)      # leftover unbalanced open marker.
+    s = s.replace("[[/term]]", "")    # leftover unbalanced close marker.
     for marker in ("**", "__", "`"):
         s = s.replace(marker, "")
     return s
 
 
+def _strip_term_markers(s: str) -> str:
+    """Remove any (balanced or leftover) glossary term markers, keeping text."""
+    s = _TERM_OPEN_RE.sub("", s)
+    return s.replace("[[/term]]", "")
+
+
 def _strip_leftover_markers(s: str) -> str:
     """Drop any unbalanced inline markers from a plain (non-span) fragment.
 
@@ -222,6 +239,118 @@ def wrap_rich(text: str, max_chars: int):
     return lines or [[("", False)]]
 
 
+def parse_inline_rich(text: str):
+    """Split ``text`` into ``[(fragment, is_bold, term_key), ...]``.
+
+    Extends :func:`parse_inline_bold` with glossary term spans
+    ``[[term:key]]visible[[/term]]``: the inner ``visible`` text is parsed for
+    ``**bold**`` as usual and every resulting fragment carries ``term_key`` so the
+    renderers can make it clickable. Text outside a term span gets ``term_key =
+    None``. Unbalanced term markers are stripped (kept identical to
+    :func:`strip_inline_md`). The concatenation of all fragment texts equals
+    ``strip_inline_md(text)`` — visible characters and wrapping are unchanged; only
+    the bold flag and the term key are added. Adjacent fragments with the same
+    (bold, term) are merged.
+    """
+    s = "" if text is None else str(text)
+    if not s:
+        return []
+    out = []
+
+    def _emit(fragment: str, bold: bool, term) -> None:
+        if fragment == "":
+            return
+        if out and out[-1][1] == bold and out[-1][2] == term:
+            out[-1] = (out[-1][0] + fragment, bold, term)
+        else:
+            out.append((fragment, bold, term))
+
+    def _emit_bolded(segment: str, term) -> None:
+        # Reuse the bold parser on a term-marker-free segment.
+        for frag, bold in parse_inline_bold(_strip_term_markers(segment)):
+            _emit(frag, bold, term)
+
+    pos = 0
+    for m in _TERM_SPAN_RE.finditer(s):
+        if m.start() > pos:
+            _emit_bolded(s[pos:m.start()], None)
+        _emit_bolded(m.group(2), m.group(1))
+        pos = m.end()
+    if pos < len(s):
+        _emit_bolded(s[pos:], None)
+    return out
+
+
+def wrap_rich_terms(text: str, max_chars: int):
+    """Like :func:`wrap_rich` but preserving glossary term keys per fragment.
+
+    Returns ``list[list[(fragment, is_bold, term_key)]]`` — one inner list per
+    output line. Wrapping is word-aware and hard-splits over-long tokens so no
+    line exceeds ``max_chars`` (the renderers measure these very lines). Term and
+    bold flags never widen a line: the visible width matches :func:`wrap`.
+    """
+    if max_chars < 1:
+        max_chars = 1
+    spans = parse_inline_rich(text)
+    if not spans:
+        return [[("", False, None)]]
+
+    tokens = []  # each: (word, bold, term) or ("\n", None, None)
+    for frag, bold, term in spans:
+        parts = frag.split("\n")
+        for pi, part in enumerate(parts):
+            if pi > 0:
+                tokens.append(("\n", None, None))
+            for word in part.split(" "):
+                if word == "":
+                    continue
+                tokens.append((word, bold, term))
+
+    lines = []
+    cur = []
+    cur_len = 0
+
+    def _flush():
+        nonlocal cur, cur_len
+        merged = []
+        for k, (word, bold, term) in enumerate(cur):
+            piece = word if k == 0 else " " + word
+            if merged and merged[-1][1] == bold and merged[-1][2] == term:
+                merged[-1] = (merged[-1][0] + piece, bold, term)
+            else:
+                merged.append((piece, bold, term))
+        lines.append(merged or [("", False, None)])
+        cur = []
+        cur_len = 0
+
+    for word, bold, term in tokens:
+        if bold is None:  # forced newline
+            _flush()
+            continue
+        if len(word) > max_chars:
+            if cur:
+                _flush()
+            chunks = _hard_split(word, max_chars)
+            for ci, chunk in enumerate(chunks):
+                if ci < len(chunks) - 1:
+                    lines.append([(chunk, bold, term)])
+                else:
+                    cur = [(chunk, bold, term)]
+                    cur_len = len(chunk)
+            continue
+        add = len(word) if cur_len == 0 else cur_len + 1 + len(word)
+        if cur_len != 0 and add > max_chars:
+            _flush()
+            cur = [(word, bold, term)]
+            cur_len = len(word)
+        else:
+            cur.append((word, bold, term))
+            cur_len = add
+    if cur:
+        _flush()
+    return lines or [[("", False, None)]]
+
+
 def parse_md_table(lines: list):
     """Parse consecutive ``| a | b |`` lines into ``(header, rows)`` or None.
 

python/functions/datascience/build_eda_render_ctx.py

@@ -20,6 +20,10 @@ vacia y el resto del ctx se construye igual. Ante un fallo global devuelve al
 menos ``{**base_ctx, "db_path": db_path, "table": table}``.
 
 Claves de DATOS que produce (las consumen los capitulos):
+  - ``head_rows``      : [ {col: valor, ...}, ... ] primeras filas CRUDAS de la
+                         tabla (``SELECT * LIMIT head_n``), una entrada por fila.
+                         La lee el capitulo OVERVIEW para mostrar df.head real en
+                         lugar del placeholder "df.head no disponible".
   - ``raw_numeric``    : {col: [float|None, ...]} muestra cruda de las columnas
                          numericas, ALINEADA POR FILA (una entrada por fila aunque
                          sea None). La leen modelos (clustering 2D en vivo) y
@@ -56,7 +60,7 @@ def _to_float(value):
         return None
 
 
-def build_eda_render_ctx(db_path, table, profile, backend="duckdb", sample=5000, base_ctx=None):
+def build_eda_render_ctx(db_path, table, profile, backend="duckdb", sample=5000, base_ctx=None, head_n=10):
     """Construye el ctx de datos crudos para los renderers de AutomaticEDA.
 
     Args:
@@ -77,13 +81,15 @@ def build_eda_render_ctx(db_path, table, profile, backend="duckdb", sample=5000,
         base_ctx: dict opcional con claves de presentacion ya preparadas
             (dataset_name, source_origin, ...). Se parte de una copia y NO se
             pisan sus claves; solo se añaden las de datos. Default None -> {}.
+        head_n: numero de filas crudas a muestrear para ``ctx["head_rows"]``
+            (df.head del capitulo OVERVIEW). Default 10. <=0 omite la clave.
 
     Returns:
         El dict ``ctx`` directamente (NO un wrapper {status,...}): se pasa tal
         cual como ``meta={"ctx": <ese dict>}`` a render_automatic_eda_pdf/pptx.
-        Nunca lanza. Claves que puede contener: raw_numeric, timeseries_raw,
+        Nunca lanza. Claves que puede contener: head_rows, raw_numeric,
-        geo_points (omitidas si no aplican o fallan), y siempre db_path + table
+        timeseries_raw, geo_points (omitidas si no aplican o fallan), y siempre
-        para backends validos.
+        db_path + table para backends validos.
     """
     # Copia de base_ctx: nunca mutamos el dict del caller. Las claves de
     # presentacion que ya traiga se conservan; las de datos se añaden encima.
@@ -117,6 +123,24 @@ def build_eda_render_ctx(db_path, table, profile, backend="duckdb", sample=5000,
         ctx["db_path"] = db_path
         ctx["table"] = table
 
+        # 1.5) head_rows: primeras filas CRUDAS de la tabla (SELECT * LIMIT n)
+        # para que el capitulo OVERVIEW muestre df.head real en vez del
+        # placeholder. Una sola query, dict-no-throw: si falla, se omite la
+        # clave (el capitulo degrada a su nota honesta). No se pisa una clave
+        # head_rows que ya viniera en base_ctx (presentacion).
+        if head_n and int(head_n) > 0 and "head_rows" not in ctx:
+            try:
+                hq = query_fn(f'SELECT * FROM "{table}" LIMIT {int(head_n)}')
+                if isinstance(hq, dict) and hq.get("status") == "ok":
+                    hrows = [
+                        dict(r) for r in (hq.get("rows") or [])
+                        if isinstance(r, dict)
+                    ]
+                    if hrows:
+                        ctx["head_rows"] = hrows
+            except Exception:  # noqa: BLE001 - dict-no-throw: omitir la clave
+                pass
+
         # 2) Columnas del perfil agregado (lectura defensiva).
         cols = profile.get("columns") if isinstance(profile, dict) else None
         cols = cols or []

python/functions/datascience/column_quality_score.md

@@ -4,10 +4,10 @@ name: column_quality_score
 kind: function
 lang: py
 domain: datascience
-version: "1.0.0"
+version: "2.0.0"
 purity: pure
 signature: "def column_quality_score(col: dict) -> dict"
-description: "Calcula un score de calidad de datos 0-100 para un ColumnProfile del grupo eda, con desglose completeness/validity/consistency y lista de issues legibles. Funcion pura, no muta el input."
+description: "Calcula un score de calidad de datos 0-100 para un ColumnProfile del grupo eda. Combina completeness (0.6) y validity (0.4) con renormalizacion por aplicabilidad; los outliers, columnas constantes e ids NO bajan el score (van a observations). Devuelve desglose por dimension, issues (defectos) y observations (señales analiticas). Funcion pura, no muta el input."
 tags: [eda, data-quality, profiling, scoring, datascience]
 uses_functions: []
 uses_types: []
@@ -17,20 +17,26 @@ error_type: ""
 imports: []
 example: |
   from datascience import column_quality_score
-  col = {"name": "precio", "inferred_type": "float", "null_pct": 0.2,
+  col = {"name": "precio", "inferred_type": "numeric", "null_pct": 0.2,
-         "unique_pct": 0.4, "flags": [], "numeric": {"outlier_pct": 0.08}}
+         "unique_pct": 0.4, "flags": [], "numeric": {"outlier_pct": 8.0}}
   column_quality_score(col)
-  # {"score": 86.8, "completeness": 0.8, "validity": 0.92,
+  # {"score": 88.0, "completeness": 0.8, "validity": 1.0,
-  #  "consistency": 1.0, "issues": ["20% nulos", "8% outliers"]}
+  #  "applicable": ["completeness", "validity"], "issues": ["20% nulos"],
+  #  "observations": ["8% de valores atípicos (z-score>3): ..."]}
 tested: true
 tests:
   - "test_clean_column_high_score"
-  - "test_half_null_lowers_completeness_and_score"
+  - "test_weights_60_40_native_type"
-  - "test_constant_column_flags_issue"
+  - "test_outliers_do_not_penalize_score"
+  - "test_nulls_lower_score_more_than_outliers"
+  - "test_validity_from_parse_rate_lowers_score"
+  - "test_validity_from_match_rate"
+  - "test_free_text_renormalizes_to_completeness_only"
+  - "test_all_null_column_scores_zero"
+  - "test_constant_column_scores_full_and_is_observation"
+  - "test_high_cardinality_id_scores_full_and_is_observation"
+  - "test_mostly_null_no_double_counts_validity"
   - "test_empty_dict_does_not_crash"
-  - "test_outliers_penalize_validity"
-  - "test_mostly_null_flag_halves_validity"
-  - "test_high_cardinality_text_flagged_as_id"
   - "test_none_values_treated_defensively"
   - "test_does_not_mutate_input"
 test_file_path: "python/functions/datascience/column_quality_score_test.py"
@@ -38,16 +44,22 @@ file_path: "python/functions/datascience/column_quality_score.py"
 params:
   - name: col
     desc: >
-      ColumnProfile dict del grupo eda (p.ej. salida de summarize_table_duckdb).
+      ColumnProfile dict del grupo eda (p.ej. salida de summarize_table_duckdb /
-      Se leen sus claves de forma defensiva con .get(...) y se toleran valores
+      profile_table). Se leen sus claves de forma defensiva con .get(...) y se
-      None. Claves usadas: null_pct (0-1), inferred_type, semantic_type,
+      toleran valores None. Claves usadas: null_pct (0-1), n_rows, empty_count
-      unique_pct (0-1), flags (list[str], reconoce "constant"/"mostly_null"),
+      (texto), inferred_type, semantic_type, validity_rate (0-1, lo expone
-      numeric ({outlier_pct: 0-1, ...}|None) y match_rate (opcional, 0-1).
+      profile_table al promocionar texto a numero/fecha), match_rate (0-1),
+      unique_pct (0-1), flags (list[str], reconoce
+      "constant"/"possible_id"/"high_cardinality") y numeric ({outlier_pct: 0-100,
+      skew, ...}|None).
 output: >
-  dict con score (float 0-100, redondeado a 1 decimal), completeness (0-1),
+  dict con score (float 0-100, 1 decimal), completeness (0-1), validity (0-1 o
-  validity (0-1), consistency (0-1) e issues (list[str] de descripciones
+  None si no aplicable), dimensions ({completeness, validity}), applicable
-  legibles de los problemas detectados). score = round(100 * (0.5*completeness
+  (list[str] de dimensiones que entraron en el score), issues (list[str] SOLO de
-  + 0.3*validity + 0.2*consistency), 1).
+  defectos de calidad: nulos, vacios, valores no conformes) y observations
+  (list[str] de señales analiticas que NO bajan el score: outliers, columna
+  constante, posible id, asimetria). score = round(100 * (0.6*completeness +
+  0.4*validity) / pesos_aplicables, 1), renormalizado cuando validity no aplica.
 ---
 
 ## Ejemplo
@@ -59,51 +71,71 @@ from datascience import column_quality_score
 col = {
     "name": "precio",
     "physical_type": "DOUBLE",
-    "inferred_type": "float",
+    "inferred_type": "numeric",
     "semantic_type": "",
-    "count": 800,
     "n_rows": 1000,
     "null_count": 200,
     "null_pct": 0.20,
     "distinct_count": 400,
     "unique_pct": 0.40,
     "flags": [],
-    "numeric": {"outlier_pct": 0.08},
+    "numeric": {"outlier_pct": 8.0, "skew": 0.3},
     "categorical": None,
     "datetime": None,
 }
 
 column_quality_score(col)
 # {
-#   "score": 86.8,
+#   "score": 88.0,            # 100 * (0.6*0.8 + 0.4*1.0)
-#   "completeness": 0.8,    # 1 - 0.20
+#   "completeness": 0.8,      # 1 - 0.20
-#   "validity": 0.92,       # 1 - min(0.08, 0.3)
+#   "validity": 1.0,          # numerica nativa: el tipo es conforme
-#   "consistency": 1.0,
+#   "dimensions": {"completeness": 0.8, "validity": 1.0},
-#   "issues": ["20% nulos", "8% outliers"],
+#   "applicable": ["completeness", "validity"],
+#   "issues": ["20% nulos"],                       # SOLO defectos de calidad
+#   "observations": ["8% de valores atípicos (z-score>3): ..."],  # NO bajan score
 # }
 ```
 
 ## Cuando usarla
 
 Cuando hayas perfilado una tabla con el grupo `eda` (p.ej.
-`summarize_table_duckdb`) y necesites un numero 0-100 por columna para
+`summarize_table_duckdb` / `profile_table`) y necesites un numero 0-100 por
-ordenar/priorizar limpieza de datos, pintar semaforos de calidad en un
+columna para ordenar/priorizar limpieza de datos, pintar semaforos de calidad,
-dashboard, o decidir que columnas descartar antes de modelar. Es la capa de
+o decidir que columnas descartar antes de modelar. Separa los **defectos de
-scoring sobre el ColumnProfile crudo: lee el perfil, no toca los datos.
+calidad reales** (`issues`: nulos, vacios, valores que no parsean a su tipo) de
+las **observaciones analiticas** (`observations`: outliers, columnas constantes,
+ids), que se reportan pero no penalizan. Es la capa de scoring sobre el
+ColumnProfile crudo: lee el perfil, no toca los datos.
 
-## Notas
+## Gotchas
 
-Funcion pura, sin I/O ni dependencias externas, no muta `col`. Lee todas las
+Funcion pura, sin I/O, no muta `col`. Aun asi conviene saber:
-claves con `.get(...)` y tolera que vengan en `None` (un ColumnProfile recien
-salido de `summarize_table_duckdb` trae muchas claves a `None`), por lo que
-nunca falla por claves ausentes — un `{}` produce un resultado bien definido.
 
-Pesos del score: completeness 0.5, validity 0.3, consistency 0.2.
+- **Los outliers NO bajan el score.** Un valor extremo puede ser real y correcto
+  (un cliente que compra mucho); detectar atipicos es analisis de la
+  distribucion, no un juicio de correccion. Salen en `observations`, no en
+  `issues`. Mismo trato para columnas constantes e identificadores de alta
+  cardinalidad: son observaciones, no defectos.
+- **`validity` puede ser `None`** (no aplicable): texto libre sin `semantic_type`
+  ni `validity_rate`, o columna 100% nula. En ese caso el score se renormaliza a
+  solo `completeness` (la columna no se premia ni castiga por algo no medible).
+- **`outlier_pct` se interpreta en escala 0-100** (la que emite
+  `describe_numeric`, z-score>3). Pasar una fraccion 0-1 produce un texto de
+  observacion con el % equivocado, pero NUNCA afecta al score.
+- **`validity_rate` lo puebla `profile_table`** al promocionar una columna de
+  texto a numero/fecha (fraccion que parsea). Si no esta presente y el tipo es
+  nativo numerico/fecha/bool, `validity = 1.0`.
+- Sin doble conteo: la falta de datos cuenta solo en `completeness` (el antiguo
+  castigo de `mostly_null` sobre `validity` se elimino).
 
-- **completeness** = `1 - null_pct` (None -> 0 nulls -> 1.0).
+## Capability growth log
-- **validity**: parte de 1.0 y penaliza `min(outlier_pct, 0.3)` en columnas
+
-  numericas, `0.5 * (1 - match_rate)` si hay `semantic_type` declarado con
+- v2.0.0 (2026-06-30) — nueva formula de calidad (report 2046): pesos 60/40
-  `match_rate` bajo disponible, y multiplica por 0.5 si el flag `mostly_null`
+  (completeness/validity) con renormalizacion por aplicabilidad; se elimina la
-  esta presente.
+  dimension `consistency`-como-informatividad y el doble castigo de
-- **consistency**: 1.0 salvo flag `constant` (-> 0.3, columna poco informativa)
+  `mostly_null`; los outliers/constantes/ids salen del score a `observations`;
-  o texto con `unique_pct > 0.9` (-> 0.6, posible id de alta cardinalidad).
+  validity mide conformidad real (parse rate / match rate / tipo nativo). Salida
+  ampliada con `dimensions`, `applicable` y `observations`.
+- v1.0.0 — version inicial: pesos 50/30/20 (completeness/validity/consistency),
+  los outliers penalizaban validity (con bug de escala) y consistency penalizaba
+  informatividad.

python/functions/datascience/column_quality_score.py

@@ -1,34 +1,78 @@
 """Score de calidad de datos (0-100) para un ColumnProfile del grupo eda.
 
 Funcion pura: dado el perfil de una columna producido por el grupo de
-capacidad `eda` (p.ej. summarize_table_duckdb), calcula un score agregado
+capacidad `eda` (p.ej. summarize_table_duckdb / profile_table), calcula un
-de calidad junto a su desglose en completeness / validity / consistency y
+score agregado de calidad junto a su desglose por dimension y dos listas
-una lista de issues legibles. No realiza I/O ni muta el input.
+legibles separadas: `issues` (defectos de calidad reales que SI bajan el
+score) y `observations` (señales analiticas que NO bajan el score). No
+realiza I/O ni muta el input.
+
+Modelo (DAMA-DMBOK / ISO 8000), ver report 2046:
+
+- Solo entran en el score las dimensiones medibles automaticamente desde el
+  perfil, sin fuente externa de verdad: completeness y validity por columna.
+- Renormalizacion por aplicabilidad: si una dimension no es medible en la
+  columna (texto libre sin semantica -> validity no aplica; columna 100% nula
+  -> validity no medible), se excluye y los pesos se renormalizan sobre las
+  aplicables. Una columna ni se premia ni se castiga por algo no medible.
+- Sin doble conteo: la falta de datos cuenta solo en completeness (se elimino
+  el antiguo castigo extra de `mostly_null` sobre validity).
+- Los OUTLIERS NO bajan la calidad. Un valor extremo puede ser real y
+  correcto; detectar atipicos es analisis de la distribucion, no un juicio de
+  coreccion. Outliers, columnas constantes e identificadores de alta
+  cardinalidad pasan a `observations`, nunca a `issues`.
 """
 
 
+# Pesos base de las dimensiones de columna (se renormalizan por aplicabilidad).
+_W_COMPLETENESS = 0.6
+_W_VALIDITY = 0.4
+
+# Tipos inferidos cuyo almacen garantiza la conformidad de tipo (validity=1.0)
+# cuando NO vienen de una promocion de texto (en cuyo caso manda validity_rate).
+_NATIVE_TYPED = ("numeric", "integer", "float", "datetime", "date", "boolean", "bool")
+
+
 def column_quality_score(col: dict) -> dict:
     """Calcula un score de calidad de datos 0-100 para un ColumnProfile.
 
-    El score pondera tres dimensiones:
+    El score combina solo dimensiones de calidad medibles desde el perfil, con
-      - completeness (0.5): proporcion de valores no nulos.
+    renormalizacion por aplicabilidad:
-      - validity     (0.3): ausencia de outliers / heuristicas de validez.
+
-      - consistency  (0.2): la columna aporta informacion (no constante, no ruido).
+      - completeness (peso base 0.6, siempre aplica): proporcion de valores
+        presentes = 1 - null_pct. En texto, las celdas vacias (`empty_count`)
+        tambien cuentan como faltantes.
+      - validity (peso base 0.4, cuando hay un criterio de validacion real):
+        fraccion de valores no nulos conformes a su tipo/semantica. Tipo nativo
+        numerico/fecha/bool = 1.0; texto promovido a numero/fecha = parse rate
+        (`validity_rate`); texto con `semantic_type` regexable = `match_rate`;
+        texto libre o columna 100% nula = NO aplicable (renormaliza a solo
+        completeness).
+
+    Los outliers, columnas constantes, identificadores y asimetria fuerte NO
+    bajan el score: se devuelven en `observations`.
 
     Args:
         col: ColumnProfile dict del grupo eda. Se leen las claves de forma
             defensiva con .get(...) y se tolera que muchas vengan en None.
-            Claves relevantes: null_pct, inferred_type, semantic_type,
+            Claves relevantes: null_pct (0-1), n_rows, empty_count,
-            unique_pct, flags (list[str]), numeric ({outlier_pct, ...}|None),
+            inferred_type, semantic_type, validity_rate (0-1, lo expone
-            match_rate (opcional).
+            profile_table al promocionar texto a numero/fecha), match_rate
+            (0-1), unique_pct (0-1), flags (list[str], reconoce
+            "constant"/"possible_id"/"high_cardinality"), numeric
+            ({outlier_pct: 0-100, skew, ...}|None).
 
     Returns:
         dict con:
-          score        (float, 0-100, redondeado a 1 decimal),
+          score        (float 0-100, redondeado a 1 decimal),
-          completeness (float, 0-1),
+          completeness (float 0-1),
-          validity     (float, 0-1),
+          validity     (float 0-1 | None si no aplicable),
-          consistency  (float, 0-1),
+          dimensions   ({completeness, validity}),
-          issues       (list[str]) descripciones legibles de los problemas.
+          applicable   (list[str] de dimensiones que entraron en el score),
+          issues       (list[str]) SOLO defectos de calidad (nulos, vacios,
+                       valores no conformes a su tipo/semantica),
+          observations (list[str]) señales analiticas que NO bajan el score
+                       (outliers, columna constante, posible id, asimetria).
     """
     if not isinstance(col, dict):
         col = {}
@@ -39,103 +83,153 @@ def column_quality_score(col: dict) -> dict:
     flags = set(flags)
 
     issues: list[str] = []
+    observations: list[str] = []
+
+    inferred_type = col.get("inferred_type") or ""
+    semantic_type = col.get("semantic_type") or ""
 
     # --- completeness -------------------------------------------------
-    null_pct = col.get("null_pct")
+    # Falta de datos = nulos + (en texto) celdas vacias. Es el unico sitio
-    if null_pct is None:
+    # donde la falta de datos cuenta: nunca se duplica en validity.
-        null_pct = 0.0
+    null_pct = _clamp(_num(col.get("null_pct"), 0.0), 0.0, 1.0)
-    try:
-        null_pct = float(null_pct)
-    except (TypeError, ValueError):
-        null_pct = 0.0
-    null_pct = _clamp(null_pct, 0.0, 1.0)
     completeness = 1.0 - null_pct
     if null_pct > 0:
-        issues.append(f"{round(null_pct * 100)}% nulos")
+        issues.append(f"{_pct(null_pct)} nulos")
 
-    # --- validity -----------------------------------------------------
+    empty_frac = 0.0
-    validity = 1.0
+    n_rows = col.get("n_rows")
-    inferred_type = col.get("inferred_type") or ""
+    empty_count = col.get("empty_count")
+    if (
+        isinstance(n_rows, (int, float)) and not isinstance(n_rows, bool) and n_rows > 0
+        and isinstance(empty_count, (int, float)) and not isinstance(empty_count, bool)
+        and empty_count > 0
+    ):
+        empty_frac = _clamp(float(empty_count) / float(n_rows), 0.0, 1.0)
+        completeness = _clamp(completeness - empty_frac, 0.0, 1.0)
+        issues.append(f"{_pct(empty_frac)} vacíos")
 
-    numeric = col.get("numeric")
+    # --- validity (con renormalizacion por aplicabilidad) -------------
-    is_numeric = inferred_type in ("integer", "float", "numeric") or isinstance(numeric, dict)
+    # None = no medible -> se excluye del score (no penaliza ni premia).
-    if isinstance(numeric, dict):
+    validity = None
-        outlier_pct = numeric.get("outlier_pct")
+    if completeness <= 0.0:
-        if outlier_pct is not None:
+        # Columna 100% faltante: no hay valores no nulos sobre los que medir
-            try:
+        # conformidad. validity no aplica -> el score sale solo de completeness
-                outlier_pct = float(outlier_pct)
+        # (= 0). Es el peor defecto de calidad posible.
-            except (TypeError, ValueError):
+        validity = None
-                outlier_pct = 0.0
-            outlier_pct = _clamp(outlier_pct, 0.0, 1.0)
-            if outlier_pct > 0:
-                penalty = min(outlier_pct, 0.3)
-                validity -= penalty
-                issues.append(f"{round(outlier_pct * 100)}% outliers")
-
-    # semantic_type declarado pero con baja tasa de match (si la conocemos).
-    semantic_type = col.get("semantic_type") or ""
-    match_rate = col.get("match_rate")
-    if semantic_type and match_rate is not None:
-        try:
-            match_rate = float(match_rate)
-        except (TypeError, ValueError):
-            match_rate = None
-        if match_rate is not None:
-            match_rate = _clamp(match_rate, 0.0, 1.0)
-            if match_rate < 1.0:
-                shortfall = 1.0 - match_rate
-                validity -= 0.5 * shortfall
-                issues.append(
-                    f"semantic_type '{semantic_type}' con baja coincidencia "
-                    f"({round(match_rate * 100)}%)"
-                )
-
-    if "mostly_null" in flags:
-        validity *= 0.5
-        issues.append("mayoritariamente nula")
-
-    validity = _clamp(validity, 0.0, 1.0)
-
-    # --- consistency --------------------------------------------------
-    consistency = 1.0
-    if "constant" in flags:
-        consistency = 0.3
-        issues.append("columna constante")
     else:
-        unique_pct = col.get("unique_pct")
+        validity_rate = col.get("validity_rate")
-        if unique_pct is not None:
+        match_rate = col.get("match_rate")
-            try:
+        if validity_rate is not None:
-                unique_pct = float(unique_pct)
+            # Texto promovido a numero/fecha: parse rate real de la muestra.
-            except (TypeError, ValueError):
+            v = _num(validity_rate, None)
-                unique_pct = None
+            if v is not None:
-        if (
+                validity = _clamp(v, 0.0, 1.0)
-            inferred_type == "text"
+                if validity < 1.0:
+                    kind = (
+                        "número" if inferred_type == "numeric"
+                        else "fecha" if inferred_type == "datetime"
+                        else inferred_type or "su tipo"
+                    )
+                    issues.append(
+                        f"{_pct(1.0 - validity)} no parsea al tipo {kind}"
+                    )
+        elif inferred_type in _NATIVE_TYPED:
+            # Tipo nativo garantizado por el almacen: no hay valores que no
+            # parseen. validity = 1.0 (no se confunde con tener outliers).
+            validity = 1.0
+        elif semantic_type and match_rate is not None:
+            v = _num(match_rate, None)
+            if v is not None:
+                validity = _clamp(v, 0.0, 1.0)
+                if validity < 1.0:
+                    issues.append(
+                        f"{_pct(1.0 - validity)} no casa con el "
+                        f"formato «{semantic_type}»"
+                    )
+        else:
+            # Texto libre / categorica sin semantica: no hay criterio honesto
+            # de validez. No aplica.
+            validity = None
+
+    # --- observations (NO bajan el score) -----------------------------
+    numeric = col.get("numeric")
+    if isinstance(numeric, dict):
+        # outlier_pct viene en escala 0-100 desde describe_numeric (z-score>3).
+        outlier_pct = _num(numeric.get("outlier_pct"), None)
+        if outlier_pct is not None and outlier_pct >= 0.05:
+            observations.append(
+                f"{_pct(outlier_pct / 100.0)} de valores atípicos (z-score>3): "
+                "revisar si son errores u observaciones legítimas"
+            )
+        skew = _num(numeric.get("skew"), None)
+        if skew is not None and abs(skew) >= 1.0:
+            observations.append(
+                f"asimetría fuerte (skew={round(skew, 2)}): considerar "
+                "re-expresión antes de modelar"
+            )
+
+    if "constant" in flags:
+        observations.append(
+            "columna constante: aporta poca información para el análisis"
+        )
+
+    unique_pct = _num(col.get("unique_pct"), None)
+    is_id = (
+        "possible_id" in flags
+        or "high_cardinality" in flags
+        or (
+            inferred_type in ("text", "categorical")
             and unique_pct is not None
             and _clamp(unique_pct, 0.0, 1.0) > 0.9
-        ):
+        )
-            consistency = 0.6
-            issues.append("posible id de alta cardinalidad")
-
-    consistency = _clamp(consistency, 0.0, 1.0)
-
-    # --- score agregado ----------------------------------------------
-    score = round(
-        100.0 * (0.5 * completeness + 0.3 * validity + 0.2 * consistency),
-        1,
     )
+    if is_id:
+        observations.append(
+            "valores casi únicos: posible identificador (no es un defecto de calidad)"
+        )
 
-    # Silencia warnings sobre la variable de tipo no usada.
+    # --- score agregado con renormalizacion ---------------------------
-    _ = is_numeric
+    applicable = ["completeness"]
+    num = _W_COMPLETENESS * completeness
+    den = _W_COMPLETENESS
+    if validity is not None:
+        applicable.append("validity")
+        num += _W_VALIDITY * validity
+        den += _W_VALIDITY
+    score = round(100.0 * num / den, 1) if den > 0 else 0.0
 
     return {
         "score": score,
         "completeness": completeness,
         "validity": validity,
-        "consistency": consistency,
+        "dimensions": {"completeness": completeness, "validity": validity},
+        "applicable": applicable,
         "issues": issues,
+        "observations": observations,
     }
 
 
+def _pct(frac: float) -> str:
+    """Formatea una fraccion 0-1 como porcentaje honesto: «N%» si >=1%, «0.N%»
+    por debajo (para no mostrar «0%» cuando hay un defecto real pequeño)."""
+    p = frac * 100.0
+    if p >= 1.0:
+        return f"{round(p)}%"
+    return f"{p:.1f}%"
+
+
+def _num(x, default):
+    """Convierte x a float; devuelve `default` si es None o no parseable."""
+    if x is None:
+        return default
+    if isinstance(x, bool):
+        return default
+    try:
+        return float(x)
+    except (TypeError, ValueError):
+        return default
+
+
 def _clamp(x: float, lo: float, hi: float) -> float:
     """Recorta x al rango [lo, hi]."""
     if x < lo:

python/functions/datascience/column_quality_score_test.py

@@ -1,4 +1,12 @@
-"""Tests para column_quality_score."""
+"""Tests para column_quality_score (nueva fórmula, report 2046).
+
+Verifica las invariantes de la fórmula de calidad:
+  - completeness (0.6) + validity (0.4) con renormalización por aplicabilidad.
+  - Los OUTLIERS no bajan el score (van a observations, no a issues).
+  - Columnas constantes e ids no bajan el score (observations).
+  - Sin doble conteo de la falta de datos.
+  - all-null -> score 0; función pura (no muta el input).
+"""
 
 import os
 import sys
@@ -9,11 +17,11 @@ from column_quality_score import column_quality_score
 
 
 def _clean_numeric_col() -> dict:
-    """ColumnProfile de una columna numerica sana, sin problemas."""
+    """ColumnProfile de una columna numérica nativa sana, sin problemas."""
     return {
         "name": "edad",
         "physical_type": "INTEGER",
-        "inferred_type": "integer",
+        "inferred_type": "numeric",
         "semantic_type": "",
         "count": 1000,
         "n_rows": 1000,
@@ -28,85 +36,163 @@ def _clean_numeric_col() -> dict:
     }
 
 
+# --------------------------------------------------------------------------- #
+# Golden
+# --------------------------------------------------------------------------- #
 def test_clean_column_high_score():
     out = column_quality_score(_clean_numeric_col())
-    assert out["score"] > 90
+    assert out["score"] == 100.0
     assert out["completeness"] == 1.0
     assert out["validity"] == 1.0
-    assert out["consistency"] == 1.0
+    assert out["applicable"] == ["completeness", "validity"]
     assert out["issues"] == []
+    assert out["observations"] == []
 
 
-def test_half_null_lowers_completeness_and_score():
+def test_weights_60_40_native_type():
+    """30% nulos en numérica nativa: score = 100*(0.6*0.7 + 0.4*1.0) = 82."""
     col = _clean_numeric_col()
-    col["null_count"] = 500
+    col["null_pct"] = 0.30
-    col["null_pct"] = 0.5
+    col["null_count"] = 300
-    clean_score = column_quality_score(_clean_numeric_col())["score"]
     out = column_quality_score(col)
-    assert out["completeness"] == 0.5
+    assert out["completeness"] == 0.7
-    assert out["score"] < clean_score
+    assert out["validity"] == 1.0
-    assert any("nulos" in issue for issue in out["issues"])
+    assert out["score"] == 82.0
+    assert any("nulos" in i for i in out["issues"])
 
 
-def test_constant_column_flags_issue():
+# --------------------------------------------------------------------------- #
+# Outliers FUERA del score
+# --------------------------------------------------------------------------- #
+def test_outliers_do_not_penalize_score():
+    """Columna con outliers pero sin nulos -> score máximo; outliers en observations."""
+    col = _clean_numeric_col()
+    col["numeric"] = {"outlier_pct": 18.0, "skew": 0.2}  # 18% atípicos (escala 0-100)
+    out = column_quality_score(col)
+    assert out["score"] == 100.0  # los outliers NO bajan la calidad
+    assert out["validity"] == 1.0
+    # No aparecen como problema de calidad...
+    assert not any("atípic" in i or "outlier" in i for i in out["issues"])
+    # ...sino como observación analítica.
+    assert any("atípic" in o for o in out["observations"])
+
+
+def test_nulls_lower_score_more_than_outliers():
+    """Vacíos sí penalizan; outliers no: comparar las dos columnas."""
+    con_nulos = _clean_numeric_col()
+    con_nulos["null_pct"] = 0.30
+    con_outliers = _clean_numeric_col()
+    con_outliers["numeric"] = {"outlier_pct": 30.0}
+    assert column_quality_score(con_nulos)["score"] < \
+        column_quality_score(con_outliers)["score"]
+
+
+# --------------------------------------------------------------------------- #
+# Validity: aplicabilidad y renormalización
+# --------------------------------------------------------------------------- #
+def test_validity_from_parse_rate_lowers_score():
+    """Numérica como texto con 20% basura: validity=0.8 -> score=92."""
+    col = {
+        "name": "precio_txt", "inferred_type": "numeric", "semantic_type": "decimal",
+        "null_pct": 0.0, "validity_rate": 0.80, "flags": [], "numeric": None,
+    }
+    out = column_quality_score(col)
+    assert out["validity"] == 0.8
+    assert out["score"] == 92.0  # 100*(0.6 + 0.4*0.8)
+    assert any("no parsea" in i for i in out["issues"])
+
+
+def test_validity_from_match_rate():
+    """Texto con semantic_type y 5% no conforme: validity=0.95."""
+    col = {
+        "name": "email", "inferred_type": "text", "semantic_type": "email",
+        "null_pct": 0.0, "match_rate": 0.95, "unique_pct": 0.5, "flags": [],
+    }
+    out = column_quality_score(col)
+    assert out["validity"] == 0.95
+    assert out["score"] == 98.0  # 100*(0.6 + 0.4*0.95)
+    assert any("no casa" in i for i in out["issues"])
+
+
+def test_free_text_renormalizes_to_completeness_only():
+    """Texto libre sin semántica: validity no aplica -> score = 100*completeness."""
+    col = {
+        "name": "comentario", "inferred_type": "text", "semantic_type": "",
+        "null_pct": 0.30, "unique_pct": 0.5, "flags": [], "numeric": None,
+    }
+    out = column_quality_score(col)
+    assert out["validity"] is None
+    assert out["applicable"] == ["completeness"]
+    assert out["completeness"] == 0.7
+    assert out["score"] == 70.0  # renormalizado a solo completeness
+
+
+# --------------------------------------------------------------------------- #
+# Casos límite (report §4.6)
+# --------------------------------------------------------------------------- #
+def test_all_null_column_scores_zero():
+    col = _clean_numeric_col()
+    col["null_pct"] = 1.0
+    col["null_count"] = 1000
+    out = column_quality_score(col)
+    assert out["completeness"] == 0.0
+    assert out["validity"] is None  # no medible sin valores no nulos
+    assert out["score"] == 0.0
+
+
+def test_constant_column_scores_full_and_is_observation():
+    """Columna constante: dato válido y completo -> score 100; baja info = observación."""
     col = _clean_numeric_col()
     col["flags"] = ["constant"]
     col["distinct_count"] = 1
     col["unique_pct"] = 0.001
     out = column_quality_score(col)
-    assert out["consistency"] == 0.3
+    assert out["score"] == 100.0  # NO se castiga la baja informatividad
-    assert any("constante" in issue for issue in out["issues"])
+    assert not any("constante" in i for i in out["issues"])
+    assert any("constante" in o for o in out["observations"])
 
 
+def test_high_cardinality_id_scores_full_and_is_observation():
+    """Id de alta cardinalidad: unicidad perfecta -> score 100; posible id = observación."""
+    col = {
+        "name": "uuid", "inferred_type": "text", "semantic_type": "",
+        "null_pct": 0.0, "unique_pct": 0.99, "flags": ["possible_id"],
+        "numeric": None,
+    }
+    out = column_quality_score(col)
+    assert out["score"] == 100.0
+    assert not any("identificador" in i for i in out["issues"])
+    assert any("identificador" in o for o in out["observations"])
+
+
+def test_mostly_null_no_double_counts_validity():
+    """85% nulos: solo completeness penaliza; validity nativa sigue 1.0 (sin doble castigo)."""
+    col = _clean_numeric_col()
+    col["null_pct"] = 0.85
+    col["flags"] = ["mostly_null"]
+    out = column_quality_score(col)
+    assert out["validity"] == 1.0  # ya no se multiplica por 0.5
+    # score = 100*(0.6*0.15 + 0.4*1.0) = 49
+    assert out["score"] == 49.0
+    assert not any("mayoritariamente" in o for o in out["observations"])
+
+
+# --------------------------------------------------------------------------- #
+# Robustez
+# --------------------------------------------------------------------------- #
 def test_empty_dict_does_not_crash():
     out = column_quality_score({})
     assert isinstance(out["score"], float)
     assert out["completeness"] == 1.0
     assert 0.0 <= out["score"] <= 100.0
     assert isinstance(out["issues"], list)
-
+    assert isinstance(out["observations"], list)
-
-def test_outliers_penalize_validity():
-    col = _clean_numeric_col()
-    col["numeric"] = {"outlier_pct": 0.2}
-    out = column_quality_score(col)
-    assert out["validity"] < 1.0
-    assert any("outliers" in issue for issue in out["issues"])
-
-
-def test_mostly_null_flag_halves_validity():
-    col = _clean_numeric_col()
-    col["null_pct"] = 0.85
-    col["flags"] = ["mostly_null"]
-    out = column_quality_score(col)
-    assert out["validity"] == 0.5
-    assert any("mayoritariamente nula" in issue for issue in out["issues"])
-
-
-def test_high_cardinality_text_flagged_as_id():
-    col = {
-        "name": "uuid",
-        "inferred_type": "text",
-        "semantic_type": "",
-        "null_pct": 0.0,
-        "unique_pct": 0.99,
-        "flags": [],
-        "numeric": None,
-    }
-    out = column_quality_score(col)
-    assert out["consistency"] < 1.0
-    assert any("alta cardinalidad" in issue for issue in out["issues"])
 
 
 def test_none_values_treated_defensively():
     col = {
-        "name": "x",
+        "name": "x", "inferred_type": None, "semantic_type": None,
-        "inferred_type": None,
+        "null_pct": None, "unique_pct": None, "flags": None, "numeric": None,
-        "semantic_type": None,
-        "null_pct": None,
-        "unique_pct": None,
-        "flags": None,
-        "numeric": None,
     }
     out = column_quality_score(col)
     assert out["completeness"] == 1.0

python/functions/datascience/detect_declared_keys_duckdb.md

@@ -0,0 +1,107 @@
+---
+name: detect_declared_keys_duckdb
+kind: function
+lang: py
+domain: datascience
+version: "1.0.0"
+purity: impure
+signature: "def detect_declared_keys_duckdb(db_path: str, table: str = None) -> dict"
+description: "Detecta las claves DECLARADAS (constraints reales) de un schema DuckDB leyendo la table function duckdb_constraints(): extrae PRIMARY KEY, FOREIGN KEY y UNIQUE (ignora NOT NULL y CHECK) y las devuelve normalizadas con sus columnas, y para las FK con su tabla y columnas referenciadas. Con table=None procesa todas las tablas; con table='X' filtra a PK/UNIQUE de X y a FK cuyo origen es X (case-sensitive). A diferencia de infer_fk_containment_duckdb (que INFIERE FKs candidatas por containment de valores cuando el schema no las declara), esta funcion devuelve las relaciones de clave REALES del schema. Estilo dict-no-throw: nunca lanza. Parte del grupo eda (relaciones de clave)."
+tags: [eda, duckdb, datascience, relations, primary-key, foreign-key, schema, exploratory-data-analysis]
+params:
+  - name: db_path
+    desc: "Ruta al archivo DuckDB. Debe existir (lectura read-only via duckdb_query_readonly; no se crea). Un path inexistente devuelve {status:'error', ...}."
+  - name: table
+    desc: "Si se pasa, filtra los resultados a esa tabla: incluye PRIMARY KEY y UNIQUE cuya tabla sea `table`, y FOREIGN KEY cuya tabla ORIGEN sea `table` (no la referenciada). None (default) devuelve los constraints de todas las tablas. La comparacion es case-sensitive (nombres tal cual los devuelve DuckDB)."
+output: "dict dict-no-throw. En exito {status:'ok', primary_keys:[{table:str, columns:[str,...]}, ...], foreign_keys:[{table:str, columns:[str,...], referenced_table:str, referenced_columns:[str,...]}, ...], unique:[{table:str, columns:[str,...]}, ...], tables:[str,...]} donde tables es la lista ordenada de tablas (origen) que poseen al menos un constraint PK/FK/UNIQUE emitido. Solo se emiten constraints de clave: NOT NULL y CHECK se ignoran. En error {status:'error', error:str}."
+uses_functions: [duckdb_query_readonly_py_infra]
+uses_types: []
+returns: []
+returns_optional: false
+error_type: "error_go_core"
+imports: []
+tested: true
+tests: ["test_golden_detecta_pks_y_fk", "test_golden_ignora_not_null_y_check", "test_edge_filtra_por_tabla_orders", "test_edge_filtra_por_tabla_customers", "test_edge_unique_declarado", "test_edge_sin_constraints_listas_vacias", "test_error_db_inexistente_no_lanza", "test_shape_resultado"]
+test_file_path: "python/functions/datascience/detect_declared_keys_duckdb_test.py"
+file_path: "python/functions/datascience/detect_declared_keys_duckdb.py"
+---
+
+## Ejemplo
+
+```python
+import sys, os, duckdb
+sys.path.insert(0, os.path.join("python", "functions"))
+from datascience import detect_declared_keys_duckdb
+
+# Base de ejemplo en /tmp: orders.customer_id -> customers.id (FK declarada)
+path = "/tmp/declared_keys_demo.duckdb"
+if os.path.exists(path):
+    os.remove(path)
+con = duckdb.connect(path)
+con.execute("CREATE TABLE customers(id INTEGER PRIMARY KEY, name TEXT)")
+con.execute(
+    "CREATE TABLE orders("
+    "  id INTEGER PRIMARY KEY,"
+    "  customer_id INTEGER REFERENCES customers(id),"
+    "  amt DOUBLE)"
+)
+con.close()
+
+res = detect_declared_keys_duckdb(path)
+if res["status"] == "ok":
+    for pk in res["primary_keys"]:
+        print(f"PK  {pk['table']}({', '.join(pk['columns'])})")
+    for fk in res["foreign_keys"]:
+        print(f"FK  {fk['table']}({', '.join(fk['columns'])}) -> "
+              f"{fk['referenced_table']}({', '.join(fk['referenced_columns'])})")
+    # PK  customers(id)
+    # PK  orders(id)
+    # FK  orders(customer_id) -> customers(id)
+else:
+    print("error:", res["error"])
+
+# Filtrar a una tabla concreta (PK/UNIQUE de orders + FK con origen orders):
+solo_orders = detect_declared_keys_duckdb(path, table="orders")
+print(solo_orders["tables"])  # ['orders']
+```
+
+## Cuando usarla
+
+- Cuando exploras un esquema DuckDB y quieres mostrar las relaciones de clave REALES (PK/FK/UNIQUE) que el schema ha declarado, sin inferir nada.
+- Como paso del capitulo RELACIONES del grupo `eda`: primero mira las claves declaradas con esta funcion; si el schema no declara FKs, complementa con `infer_fk_containment_duckdb` (inferencia por containment).
+- Antes de documentar o migrar un esquema, para listar el contrato de integridad referencial que el motor ya conoce.
+- Para validar que las constraints que esperas (esa FK que creaste con `REFERENCES`) realmente estan declaradas en la base materializada.
+
+## Gotchas
+
+- **Impura**: lee de disco via la primitiva read-only `duckdb_query_readonly` (no crea ni modifica la base). El `db_path` debe existir; un path inexistente devuelve `{status:'error'}` (read_only NO crea la base).
+- **Requiere `duckdb_constraints()`**: usa la table function `duckdb_constraints()`, disponible en DuckDB modernos (verificado en 1.5.2). En versiones antiguas sin esa funcion, la query falla y se devuelve `{status:'error'}`.
+- **Solo claves DECLARADAS**: devuelve lo que el schema declaro con `PRIMARY KEY` / `FOREIGN KEY (... REFERENCES ...)` / `UNIQUE`. Una tabla materializada con `CREATE TABLE AS SELECT` NO lleva constraints — para esos casos no habra claves que mostrar y hay que INFERIRLAS (`infer_fk_containment_duckdb`).
+- **NOT NULL y CHECK se ignoran**: `duckdb_constraints()` tambien emite filas `NOT NULL` (DuckDB genera una por cada columna PK) y `CHECK`; esta funcion las descarta y solo conserva PK/FK/UNIQUE.
+- **Nombres case-sensitive**: el filtro `table='Orders'` no casa con una tabla `orders`. Se comparan los nombres tal cual los devuelve DuckDB.
+- **FK atribuida al origen**: una FOREIGN KEY se atribuye a su tabla ORIGEN (el `table` de la entrada), no a la referenciada. El filtro `table='X'` trae las FK cuyo origen es X, no las que apuntan a X.
+- **`tables` = tablas dueñas de constraints emitidos**: la lista `tables` contiene solo las tablas que poseen al menos un PK/FK/UNIQUE en el resultado (su campo `table`), ordenadas. No incluye tablas referenciadas que no tengan constraint propio en la salida.
+- **Columnas como listas**: `constraint_column_names` y `referenced_column_names` son columnas LIST de DuckDB; en 1.5.2 llegan como listas Python. La funcion las normaliza a listas de strings con una red de seguridad por si llegaran como string.
+
+## Notas
+
+`duckdb_constraints()` devuelve una fila por constraint con los campos
+`table_name`, `constraint_type`, `constraint_column_names`, `referenced_table`,
+`referenced_column_names`. Mapeo a la salida:
+
+```text
+PRIMARY KEY -> primary_keys[]: {table, columns}
+UNIQUE      -> unique[]:       {table, columns}
+FOREIGN KEY -> foreign_keys[]: {table, columns, referenced_table, referenced_columns}
+NOT NULL    -> ignorado
+CHECK       -> ignorado
+```
+
+Para una FK, `referenced_table` y `referenced_column_names` vienen poblados; para
+PK/UNIQUE, `referenced_table` es NULL y `referenced_column_names` una lista vacia.
+
+Complementa a `infer_fk_containment_duckdb`: esta funcion devuelve las relaciones
+de clave REALES del schema (declaradas); la otra INFIERE FKs candidatas por
+containment de valores cuando el schema no las declaro. En el capitulo RELACIONES
+de AutomaticEDA se usan en orden: primero las declaradas, luego la inferencia como
+respaldo.

python/functions/datascience/detect_declared_keys_duckdb.py

@@ -0,0 +1,127 @@
+"""detect_declared_keys_duckdb — lee las claves DECLARADAS de un schema DuckDB.
+
+Funcion impura: lee de disco a traves de la primitiva read-only del grupo
+`duckdb` (duckdb_query_readonly). Pertenece al grupo de capacidad `eda`
+(relaciones de clave): a diferencia de infer_fk_containment_duckdb, que INFIERE
+FOREIGN KEYs candidatas por containment de valores, esta funcion devuelve las
+constraints REALES que el schema ha declarado (PRIMARY KEY / FOREIGN KEY /
+UNIQUE) leyendo la table function `duckdb_constraints()`.
+
+Es la pieza del capitulo RELACIONES de AutomaticEDA que muestra las relaciones de
+clave reales cuando existen — frente a la inferencia, que se usa cuando el schema
+no las declaro.
+
+Estilo dict-no-throw del grupo duckdb: nunca lanza; captura cualquier error y
+devuelve {status:'error', error:str}.
+"""
+
+from infra import duckdb_query_readonly
+
+
+def _as_list(value) -> list:
+    """Normaliza el valor de una columna LIST de DuckDB a una lista de strings.
+
+    En DuckDB 1.5.2, `constraint_column_names` y `referenced_column_names` llegan
+    ya como listas Python a traves de duckdb_query_readonly. Este helper es solo
+    una red de seguridad: si por cualquier motivo llegara como string (p.ej. la
+    representacion `[id, customer_id]`), la parsea de forma defensiva.
+    """
+    if value is None:
+        return []
+    if isinstance(value, (list, tuple)):
+        return [str(v) for v in value]
+    if isinstance(value, str):
+        s = value.strip()
+        if s.startswith("[") and s.endswith("]"):
+            s = s[1:-1]
+        if not s.strip():
+            return []
+        return [
+            part.strip().strip("'\"")
+            for part in s.split(",")
+            if part.strip().strip("'\"")
+        ]
+    return [str(value)]
+
+
+def detect_declared_keys_duckdb(db_path: str, table: str = None) -> dict:
+    """Detecta las claves PRIMARY KEY / FOREIGN KEY / UNIQUE declaradas en DuckDB.
+
+    Lee la table function `duckdb_constraints()` y extrae solo las constraints de
+    clave (PRIMARY KEY, FOREIGN KEY, UNIQUE), ignorando NOT NULL y CHECK.
+
+    Args:
+        db_path: ruta al archivo DuckDB. Debe existir (lectura read-only; no se
+            crea). Un path inexistente devuelve {status:'error', ...} sin lanzar.
+        table: si se pasa, filtra los resultados a esa tabla: incluye PRIMARY KEY
+            y UNIQUE cuya tabla sea `table`, y FOREIGN KEY cuya tabla ORIGEN sea
+            `table`. None (default) devuelve los constraints de todas las tablas.
+            La comparacion de nombres es case-sensitive (tal cual los devuelve
+            DuckDB).
+
+    Returns:
+        dict dict-no-throw. En exito:
+            {status:'ok',
+             primary_keys:[{table:str, columns:[str, ...]}, ...],
+             foreign_keys:[{table:str, columns:[str, ...],
+                            referenced_table:str,
+                            referenced_columns:[str, ...]}, ...],
+             unique:[{table:str, columns:[str, ...]}, ...],
+             tables:[str, ...]}   # tablas (origen) con algun PK/FK/UNIQUE emitido
+        En error (sin lanzar): {status:'error', error:str}.
+    """
+    try:
+        sql = (
+            "SELECT table_name, constraint_type, constraint_column_names, "
+            "referenced_table, referenced_column_names FROM duckdb_constraints()"
+        )
+        res = duckdb_query_readonly(db_path, sql)
+        if res["status"] != "ok":
+            return {"status": "error", "error": res["error"]}
+
+        primary_keys = []
+        foreign_keys = []
+        unique = []
+        tables = set()
+
+        for row in res["rows"]:
+            ctype = row["constraint_type"]
+            tname = row["table_name"]
+
+            # Filtro por tabla origen: para PK/FK/UNIQUE el dueño del constraint es
+            # `table_name`. Una FK se atribuye a su tabla origen (no a la
+            # referenciada), igual que el filtro pide.
+            if table is not None and tname != table:
+                continue
+
+            cols = _as_list(row["constraint_column_names"])
+
+            if ctype == "PRIMARY KEY":
+                primary_keys.append({"table": tname, "columns": cols})
+                tables.add(tname)
+            elif ctype == "UNIQUE":
+                unique.append({"table": tname, "columns": cols})
+                tables.add(tname)
+            elif ctype == "FOREIGN KEY":
+                foreign_keys.append(
+                    {
+                        "table": tname,
+                        "columns": cols,
+                        "referenced_table": row["referenced_table"],
+                        "referenced_columns": _as_list(
+                            row["referenced_column_names"]
+                        ),
+                    }
+                )
+                tables.add(tname)
+            # NOT NULL y CHECK se ignoran: no son relaciones de clave.
+
+        return {
+            "status": "ok",
+            "primary_keys": primary_keys,
+            "foreign_keys": foreign_keys,
+            "unique": unique,
+            "tables": sorted(tables),
+        }
+    except Exception as e:  # noqa: BLE001
+        return {"status": "error", "error": str(e)}

python/functions/datascience/detect_declared_keys_duckdb_test.py

@@ -0,0 +1,167 @@
+"""Tests para detect_declared_keys_duckdb."""
+
+import duckdb
+import pytest
+
+from .detect_declared_keys_duckdb import detect_declared_keys_duckdb
+
+
+@pytest.fixture
+def db(tmp_path):
+    """DuckDB temporal con claves declaradas.
+
+    - customers(id PRIMARY KEY, name)
+    - orders(id PRIMARY KEY, customer_id REFERENCES customers(id), amt)
+
+    Esto declara dos PRIMARY KEY (customers.id, orders.id) y una FOREIGN KEY
+    (orders.customer_id -> customers.id). DuckDB ademas genera constraints
+    NOT NULL para las columnas PK, que la funcion debe ignorar.
+    """
+    path = str(tmp_path / "keys_test.duckdb")
+    con = duckdb.connect(path)
+    con.execute("CREATE TABLE customers(id INTEGER PRIMARY KEY, name TEXT)")
+    con.execute(
+        "CREATE TABLE orders("
+        "  id INTEGER PRIMARY KEY,"
+        "  customer_id INTEGER REFERENCES customers(id),"
+        "  amt DOUBLE"
+        ")"
+    )
+    con.close()
+    return path
+
+
+def _pk_for(res, table):
+    """Devuelve la entrada primary_keys cuya tabla es `table`, o None."""
+    for pk in res["primary_keys"]:
+        if pk["table"] == table:
+            return pk
+    return None
+
+
+def test_golden_detecta_pks_y_fk(db):
+    """Golden: detecta las dos PK y la FK declaradas, con valores concretos."""
+    res = detect_declared_keys_duckdb(db)
+    assert res["status"] == "ok"
+
+    # PRIMARY KEY de customers y de orders.
+    pk_customers = _pk_for(res, "customers")
+    pk_orders = _pk_for(res, "orders")
+    assert pk_customers is not None
+    assert pk_customers["columns"] == ["id"]
+    assert pk_orders is not None
+    assert pk_orders["columns"] == ["id"]
+
+    # FOREIGN KEY orders.customer_id -> customers.id.
+    assert len(res["foreign_keys"]) == 1
+    fk = res["foreign_keys"][0]
+    assert fk["table"] == "orders"
+    assert fk["columns"] == ["customer_id"]
+    assert fk["referenced_table"] == "customers"
+    assert fk["referenced_columns"] == ["id"]
+
+    # tables incluye ambas (origen de algun constraint).
+    assert res["tables"] == ["customers", "orders"]
+
+
+def test_golden_ignora_not_null_y_check(db):
+    """NOT NULL (auto-generado por las PK) no aparece como clave."""
+    res = detect_declared_keys_duckdb(db)
+    assert res["status"] == "ok"
+    # Solo 2 PK reales (no las NOT NULL que DuckDB genera por cada columna PK).
+    assert len(res["primary_keys"]) == 2
+    # No hay UNIQUE declarado en este schema.
+    assert res["unique"] == []
+
+
+def test_edge_filtra_por_tabla_orders(db):
+    """Edge table='orders': PK de orders + su FK; NO la PK de customers."""
+    res = detect_declared_keys_duckdb(db, table="orders")
+    assert res["status"] == "ok"
+
+    # Solo la PK de orders.
+    assert len(res["primary_keys"]) == 1
+    assert res["primary_keys"][0]["table"] == "orders"
+    assert res["primary_keys"][0]["columns"] == ["id"]
+    # La PK de customers NO esta.
+    assert _pk_for(res, "customers") is None
+
+    # La FK de orders si esta (origen = orders).
+    assert len(res["foreign_keys"]) == 1
+    assert res["foreign_keys"][0]["table"] == "orders"
+    assert res["foreign_keys"][0]["referenced_table"] == "customers"
+
+    # tables solo contiene orders (la dueña de los constraints emitidos).
+    assert res["tables"] == ["orders"]
+
+
+def test_edge_filtra_por_tabla_customers(db):
+    """Edge table='customers': solo su PK; ninguna FK (orders queda fuera)."""
+    res = detect_declared_keys_duckdb(db, table="customers")
+    assert res["status"] == "ok"
+    assert len(res["primary_keys"]) == 1
+    assert res["primary_keys"][0]["table"] == "customers"
+    assert res["foreign_keys"] == []
+    assert res["tables"] == ["customers"]
+
+
+def test_edge_unique_declarado(tmp_path):
+    """Edge: una constraint UNIQUE declarada aparece en `unique`."""
+    path = str(tmp_path / "unique_test.duckdb")
+    con = duckdb.connect(path)
+    con.execute("CREATE TABLE products(sku INTEGER UNIQUE, name TEXT)")
+    con.close()
+
+    res = detect_declared_keys_duckdb(path)
+    assert res["status"] == "ok"
+    assert len(res["unique"]) == 1
+    assert res["unique"][0]["table"] == "products"
+    assert res["unique"][0]["columns"] == ["sku"]
+    assert res["primary_keys"] == []
+    assert res["foreign_keys"] == []
+    assert res["tables"] == ["products"]
+
+
+def test_edge_sin_constraints_listas_vacias(tmp_path):
+    """Edge: tabla sin PK/FK/UNIQUE -> todas las listas vacias, status ok."""
+    path = str(tmp_path / "no_keys.duckdb")
+    con = duckdb.connect(path)
+    con.execute("CREATE TABLE log(a INTEGER, b INTEGER)")
+    con.close()
+
+    res = detect_declared_keys_duckdb(path)
+    assert res["status"] == "ok"
+    assert res["primary_keys"] == []
+    assert res["foreign_keys"] == []
+    assert res["unique"] == []
+    assert res["tables"] == []
+
+
+def test_error_db_inexistente_no_lanza(tmp_path):
+    """Error: db_path inexistente -> status error, sin lanzar excepcion."""
+    path = str(tmp_path / "does_not_exist.duckdb")
+    res = detect_declared_keys_duckdb(path)
+    assert res["status"] == "error"
+    assert isinstance(res["error"], str)
+    assert res["error"] != ""
+
+
+def test_shape_resultado(db):
+    """El retorno tiene exactamente las claves esperadas."""
+    res = detect_declared_keys_duckdb(db)
+    assert set(res.keys()) == {
+        "status",
+        "primary_keys",
+        "foreign_keys",
+        "unique",
+        "tables",
+    }
+    for pk in res["primary_keys"]:
+        assert set(pk.keys()) == {"table", "columns"}
+    for fk in res["foreign_keys"]:
+        assert set(fk.keys()) == {
+            "table",
+            "columns",
+            "referenced_table",
+            "referenced_columns",
+        }

python/functions/datascience/pptx_link_run_to_slide.md

@@ -0,0 +1,85 @@
+---
+name: pptx_link_run_to_slide
+kind: function
+lang: py
+domain: datascience
+version: "1.0.0"
+purity: impure
+signature: "def pptx_link_run_to_slide(run, source_slide, target_slide) -> bool"
+description: "Convierte un run de texto de python-pptx en un hyperlink INTERNO 'ir a la diapositiva'. python-pptx soporta run.hyperlink.address para URLs externas pero NO para saltar a otra slide del mismo deck; esta función crea ese salto manipulando el XML: añade una relación slide->slide (RT.SLIDE) y un <a:hlinkClick> con action='ppaction://hlinksldjump' y el r:id de la relación, insertado como primer hijo del <a:rPr> del run (orden del schema CT_TextCharacterProperties). Idempotente (elimina un hlinkClick previo antes de insertar). Al pulsar el texto en PowerPoint o visores compatibles se navega a target_slide. Motor python-pptx. No lanza nunca: cualquier excepción -> return False."
+tags: [eda, pptx, hyperlink, slide-jump, navigation, glossary, automatic-eda, python-pptx, xml, datascience, python]
+uses_functions: []
+uses_types: []
+returns: []
+returns_optional: false
+error_type: "error_go_core"
+imports: ["python-pptx"]
+params:
+  - name: run
+    desc: "el pptx.text.text._Run cuyo texto se vuelve clicable. Debe pertenecer a un run real (expone ._r, el elemento <a:r>). Un objeto sin ._r hace que la función devuelva False sin lanzar."
+  - name: source_slide
+    desc: "la Slide que contiene el run. Su part recibe la relación slide->slide (relate_to con RELATIONSHIP_TYPE.SLIDE); el r:id resultante se referencia en el hlinkClick."
+  - name: target_slide
+    desc: "la Slide de destino del salto. Debe pertenecer al MISMO Presentation que source_slide para que la relación interna sea válida."
+output: "bool. True si se aplicó el hyperlink interno (relación creada + <a:hlinkClick> insertado en el rPr del run); False si algo lo impidió (run inválido, slides de presentaciones distintas, etc.). Nunca lanza."
+tested: true
+tests: ["test_golden_run_se_vuelve_salto_a_otra_slide", "test_idempotente_reaplica_sin_duplicar_hlinkclick", "test_error_path_run_invalido_devuelve_false_sin_lanzar"]
+test_file_path: "python/functions/datascience/pptx_link_run_to_slide_test.py"
+file_path: "python/functions/datascience/pptx_link_run_to_slide.py"
+---
+
+## Ejemplo
+
+```python
+from pptx import Presentation
+from pptx.util import Inches
+from pptx.oxml.ns import qn
+
+from datascience.pptx_link_run_to_slide import pptx_link_run_to_slide
+
+prs = Presentation()
+blank = prs.slide_layouts[6]            # layout en blanco
+slide0 = prs.slides.add_slide(blank)
+slide1 = prs.slides.add_slide(blank)    # destino del salto (p.ej. el glosario)
+
+box = slide0.shapes.add_textbox(Inches(1), Inches(1), Inches(4), Inches(1))
+run = box.text_frame.paragraphs[0].add_run()
+run.text = "ir al glosario"
+
+ok = pptx_link_run_to_slide(run, slide0, slide1)
+print(ok)  # -> True
+
+# El run quedó con <a:rPr><a:hlinkClick action="ppaction://hlinksldjump" r:id="rIdN"/></a:rPr>
+hlink = run._r.get_or_add_rPr().find(qn("a:hlinkClick"))
+print(hlink.get("action"))  # -> ppaction://hlinksldjump
+prs.save("deck_con_salto.pptx")
+```
+
+## Cuando usarla
+
+Cuando construyas un deck PPTX con **navegación interna** y quieras que un texto salte a
+otra diapositiva al pulsarlo: un **glosario clicable** (cada término enlaza a su slide de
+definición), un **índice/tabla de contenidos navegable**, botones "volver a la portada", o
+referencias cruzadas entre capítulos. Es la pieza que `python-pptx` no cubre de fábrica —
+úsala sobre los runs ya creados por renderers como `render_automatic_eda_pptx` del grupo
+`eda` para enriquecer el deck con saltos sin reescribir el XML a mano cada vez.
+
+## Gotchas
+
+- **Impura**: muta el XML del run y crea una relación nueva en el part de `source_slide`.
+- **Solo navega en visores que respetan `ppaction://hlinksldjump`**: PowerPoint y la
+  mayoría de visores compatibles lo siguen; algunos visores web/ligeros lo ignoran (el
+  texto se ve igual pero no salta).
+- **Mismo Presentation**: `source_slide` y `target_slide` deben pertenecer al mismo deck.
+  Si son de presentaciones distintas, la relación interna no es válida y el salto no
+  funcionará (la función puede devolver True por crear la relación, pero el resultado en
+  el visor no será el esperado).
+- **El `<a:hlinkClick>` vive en el `<a:rPr>` del run**, no como hijo directo del `<a:r>`.
+  Para localizarlo: `run._r.get_or_add_rPr().find(qn("a:hlinkClick"))` (un `find` sobre
+  `run._r` devuelve `None` porque solo mira hijos directos del `<a:r>`).
+- **Idempotente**: si el run ya tenía un `hlinkClick` (p.ej. una URL externa o un salto
+  previo), se elimina antes de insertar el nuevo — un run tiene como mucho un click-link.
+- **Nunca lanza**: cualquier excepción (run sin `._r`, slides incompatibles, etc.) se
+  traga y devuelve `False`. Comprobar el booleano si el salto es crítico.
+- **Dependencia python-pptx**: declarada en `python/pyproject.toml`. Tests con
+  `~/fn_registry/python/.venv/bin/python3` (tiene `python-pptx` instalado).

python/functions/datascience/pptx_link_run_to_slide.py

@@ -0,0 +1,50 @@
+"""Convierte un run de texto de python-pptx en un hyperlink interno "ir a la diapositiva".
+
+python-pptx expone ``run.hyperlink.address`` para URLs externas, pero NO ofrece una
+API pública para saltar a otra diapositiva del mismo deck. Esta función crea ese salto
+interno manipulando el XML: añade una relación ``slide -> slide`` y un
+``<a:hlinkClick>`` con la acción ``ppaction://hlinksldjump`` en el run, de modo que al
+pulsar el texto en PowerPoint (o en visores que respetan esa acción) se navega a la
+diapositiva de destino.
+"""
+
+from pptx.opc.constants import RELATIONSHIP_TYPE as RT
+from pptx.oxml.ns import qn
+
+
+def pptx_link_run_to_slide(run, source_slide, target_slide) -> bool:
+    """Convierte un run de texto en un hyperlink interno "ir a la diapositiva".
+
+    Añade una relación ``slide -> slide`` desde la slide origen al part de la slide
+    destino y crea un ``<a:hlinkClick>`` con ``action="ppaction://hlinksldjump"`` como
+    primer hijo del ``<a:rPr>`` del run (orden válido del schema
+    ``CT_TextCharacterProperties``). La operación es idempotente: un ``hlinkClick``
+    previo en el mismo run se elimina antes de insertar el nuevo.
+
+    Args:
+        run: el ``pptx.text.text._Run`` cuyo texto se vuelve clicable.
+        source_slide: la ``Slide`` que contiene el run.
+        target_slide: la ``Slide`` de destino del salto.
+
+    Returns:
+        True si se aplicó el hyperlink; False si algo impidió aplicarlo (no lanza).
+    """
+    try:
+        rId = source_slide.part.relate_to(target_slide.part, RT.SLIDE)
+        rPr = run._r.get_or_add_rPr()
+        # Elimina un hlinkClick previo si lo hubiera (idempotente).
+        for existing in rPr.findall(qn("a:hlinkClick")):
+            rPr.remove(existing)
+        hlink = rPr.makeelement(
+            qn("a:hlinkClick"),
+            {
+                qn("r:id"): rId,
+                "action": "ppaction://hlinksldjump",
+            },
+        )
+        # a:hlinkClick debe ir como primer hijo de rPr
+        # (orden del schema CT_TextCharacterProperties).
+        rPr.insert(0, hlink)
+        return True
+    except Exception:
+        return False

python/functions/datascience/pptx_link_run_to_slide_test.py

@@ -0,0 +1,73 @@
+"""Tests for pptx_link_run_to_slide — salto interno run -> diapositiva.
+
+Self-contained: construye una Presentation en memoria con dos slides en blanco,
+un textbox con un run en la slide 0, y verifica que la función inyecta un
+``<a:hlinkClick>`` con ``action="ppaction://hlinksldjump"`` y un ``r:id`` que
+resuelve al part de la slide 1.
+"""
+
+import pytest
+
+pytest.importorskip("pptx")
+
+from pptx import Presentation  # noqa: E402
+from pptx.oxml.ns import qn  # noqa: E402
+from pptx.util import Inches  # noqa: E402
+
+from datascience.pptx_link_run_to_slide import pptx_link_run_to_slide  # noqa: E402
+
+
+def _two_slide_deck_with_run():
+    prs = Presentation()
+    blank = prs.slide_layouts[6]  # layout en blanco
+    slide0 = prs.slides.add_slide(blank)
+    slide1 = prs.slides.add_slide(blank)
+
+    box = slide0.shapes.add_textbox(Inches(1), Inches(1), Inches(4), Inches(1))
+    tf = box.text_frame
+    para = tf.paragraphs[0]
+    run = para.add_run()
+    run.text = "ir al glosario"
+    return prs, slide0, slide1, run
+
+
+def test_golden_run_se_vuelve_salto_a_otra_slide():
+    prs, slide0, slide1, run = _two_slide_deck_with_run()
+
+    ok = pptx_link_run_to_slide(run, slide0, slide1)
+    assert ok is True
+
+    # El hlinkClick es hijo del rPr del run (orden del schema
+    # CT_TextCharacterProperties), no hijo directo del <a:r>.
+    rPr = run._r.get_or_add_rPr()
+    hlink = rPr.find(qn("a:hlinkClick"))
+    assert hlink is not None
+    assert hlink.get("action") == "ppaction://hlinksldjump"
+
+    rId = hlink.get(qn("r:id"))
+    assert rId, "el hlinkClick debe llevar un r:id no vacío"
+
+    # El rId debe existir en las relaciones de la slide origen y apuntar
+    # al part de la slide destino.
+    rels = slide0.part.rels
+    assert rId in rels
+    assert rels[rId].target_part is slide1.part
+
+
+def test_idempotente_reaplica_sin_duplicar_hlinkclick():
+    prs, slide0, slide1, run = _two_slide_deck_with_run()
+
+    assert pptx_link_run_to_slide(run, slide0, slide1) is True
+    assert pptx_link_run_to_slide(run, slide0, slide1) is True
+
+    rPr = run._r.get_or_add_rPr()
+    hlinks = rPr.findall(qn("a:hlinkClick"))
+    assert len(hlinks) == 1
+
+
+def test_error_path_run_invalido_devuelve_false_sin_lanzar():
+    prs, slide0, slide1, _run = _two_slide_deck_with_run()
+
+    # Un objeto sin ._r ni soporte de relación -> la función no lanza, devuelve False.
+    ok = pptx_link_run_to_slide(object(), slide0, slide1)
+    assert ok is False

python/functions/datascience/suggest_intratable_fk_candidates.md

@@ -0,0 +1,91 @@
+---
+name: suggest_intratable_fk_candidates
+kind: function
+lang: py
+domain: datascience
+version: "1.0.0"
+purity: pure
+signature: "def suggest_intratable_fk_candidates(profile: dict, max_candidates: int = 20) -> list"
+description: "Sobre el TableProfile de UNA tabla (el dict de profile_table), sugiere por heuristica de nombre + cardinalidad que columnas PARECEN una clave foranea hacia otra tabla, cuando no hay relaciones inter-tabla que medir (una sola tabla). Es una SUGERENCIA, no una afirmacion: el ref_table_guess es el stem del nombre (customer_id -> customer) y NO confirma containment. Pura: solo lee el dict, sin I/O; nunca lanza (devuelve [])."
+tags: [eda, datascience, relationships, foreign-key, fk, heuristic, schema, python]
+uses_functions: []
+uses_types: []
+returns: []
+returns_optional: false
+error_type: ""
+imports: []
+params:
+  - name: profile
+    desc: "TableProfile (dict que produce profile_table / summarize_table_*). Se leen de forma defensiva `columns` (lista de ColumnProfile con name/inferred_type/physical_type/distinct_count/unique_pct/flags), `n_rows` (int) y `key_candidates` (lista de nombres de columna ya candidatos a PK, que se excluyen). Si no es dict o no trae columns -> []."
+  - name: max_candidates
+    desc: "Tope de sugerencias devueltas (default 20). Las columnas candidatas se ordenan por distinct_count descendente (mas informativas primero) antes de cortar a este maximo."
+output: "list (posiblemente vacia) de dicts, uno por columna sugerida, con claves: `column` (nombre), `ref_table_guess` (tabla conjeturada por el stem del nombre, p.ej. customer_id -> 'customer'), `reason` (frase humana que deja claro que es heuristica sin confirmar containment), `distinct_count` (int|None), `unique_pct` (float|None, fraccion 0-1 tal como viene del profile), `inferred_type` (str), `physical_type` (str). Nunca lanza."
+tested: true
+tests: ["test_golden_customer_id_detectado_otras_no", "test_camelcase_albumid_detectado", "test_constante_status_id_no_aparece", "test_profile_vacio_y_none_devuelven_lista_vacia", "test_category_id_casi_unico_parece_pk_no_aparece", "test_ref_table_guess_multitoken_y_orden_por_distinct", "test_max_candidates_corta_la_lista", "test_id_generico_solo_nunca_es_fk"]
+test_file_path: "python/functions/datascience/suggest_intratable_fk_candidates_test.py"
+file_path: "python/functions/datascience/suggest_intratable_fk_candidates.py"
+---
+
+## Ejemplo
+
+```python
+from datascience import suggest_intratable_fk_candidates
+
+# TableProfile de UNA tabla (tipo titanic): customer_id es FK N:1; id es la PK;
+# amount es una medida float; name es categorica sin sufijo de id.
+profile = {
+    "n_rows": 891,
+    "key_candidates": ["id"],
+    "columns": [
+        {"name": "id", "inferred_type": "numeric", "physical_type": "BIGINT",
+         "distinct_count": 891, "unique_pct": 1.0, "flags": ["possible_id"]},
+        {"name": "customer_id", "inferred_type": "numeric", "physical_type": "BIGINT",
+         "distinct_count": 137, "unique_pct": 0.15, "flags": []},
+        {"name": "amount", "inferred_type": "numeric", "physical_type": "DOUBLE",
+         "distinct_count": 400, "unique_pct": 0.45, "flags": []},
+        {"name": "name", "inferred_type": "categorical", "physical_type": "VARCHAR",
+         "distinct_count": 700, "unique_pct": 0.78, "flags": []},
+    ],
+}
+
+out = suggest_intratable_fk_candidates(profile)
+[c["column"] for c in out]              # -> ["customer_id"]
+out[0]["ref_table_guess"]               # -> "customer"
+out[0]["reason"]
+# -> "el nombre termina en '_id' y es N:1 (137 valores distintos < 891 filas):
+#     parece (heuristica por nombre, sin confirmar containment) una referencia a
+#     una tabla «customer»"
+```
+
+## Cuando usarla
+
+Cuando el EDA tiene SOLO UNA tabla y, por tanto, no se puede inferir una FK
+inter-tabla por containment (no hay otra tabla cuyos valores contener). Es el plan B
+del capitulo RELACIONES de AutomaticEDA: en vez de medir solapamiento de valores
+entre tablas (lo correcto cuando hay varias, ver `infer_fk_containment_duckdb` /
+`build_join_graph`), conjetura por el NOMBRE de la columna (`<algo>_id`) y por su
+CARDINALIDAD N:1 que columnas parecen apuntar a una entidad externa. Usala para
+enriquecer el reporte con "estas columnas parecen referencias a otras tablas" sin
+prometer que esa tabla exista. NO la uses si tienes varias tablas: ahi mide
+containment de verdad.
+
+## Gotchas
+
+- Es **heuristica**, no una verdad: produce **falsos positivos** (una columna
+  `period_id` que en realidad es un codigo libre, no una FK) y **falsos negativos**
+  (una FK que no se llama `*_id`, p.ej. `parent`, `owner`, `sku`). No la trates como
+  una afirmacion de esquema.
+- `ref_table_guess` es una **conjetura por el nombre** (el stem sin el sufijo id):
+  `customer_id` -> `customer`, `AlbumId` -> `album`, `manager_staff_id` ->
+  `manager_staff`. Puede no coincidir con el nombre real de la tabla (plurales,
+  prefijos, alias). Es una pista, no un join garantizado.
+- **NO confirma containment**: no comprueba que los valores de la columna existan en
+  ninguna otra tabla (no puede — solo recibe el perfil de una tabla). Para confirmar
+  una FK real con varias tablas usa `infer_fk_containment_duckdb`.
+- Excluye deliberadamente: el `id`/`Id`/`ID` generico a secas (suele ser la PK
+  propia, no una referencia), las columnas constantes, las que parecen unicas
+  (`unique_pct >= 0.99`, mas PK que FK) y los tipos no-clave (float/decimal son
+  medidas; date/time/timestamp y boolean no son claves). En camelCase, `paid`,
+  `valid`, `grid` (con `id` en minuscula y sin separador) NO se confunden con FK.
+- `unique_pct` se interpreta como **fraccion 0-1** (tal como la emite el profile), no
+  como porcentaje 0-100.

python/functions/datascience/suggest_intratable_fk_candidates.py

@@ -0,0 +1,202 @@
+"""suggest_intratable_fk_candidates — heuristica de FK intra-tabla del grupo `eda`.
+
+Sobre el TableProfile de UNA tabla (el dict que produce ``profile_table``), sugiere
+por heuristica de NOMBRE + CARDINALIDAD que columnas PARECEN una clave foranea hacia
+otra tabla, util cuando no hay relaciones inter-tabla disponibles (una sola tabla y,
+por tanto, sin containment cruzado que medir). Es una SUGERENCIA, no una afirmacion:
+no confirma que exista la tabla referida ni que los valores esten contenidos en ella.
+
+La consume el capitulo RELACIONES de AutomaticEDA cuando solo hay una tabla.
+
+Funcion PURA: solo lee el dict (lectura defensiva con ``.get``), no hace I/O y nunca
+lanza por inputs raros (devuelve ``[]``).
+"""
+
+# inferred_type que es compatible con una clave foranea (entero/categorico).
+_FK_INFERRED_OK = {"numeric", "categorical", "integer"}
+
+# Prefijos de physical_type que admiten ser clave foranea (enteros, texto, uuid).
+_FK_PHYSICAL_PREFIXES = (
+    "int", "bigint", "smallint", "tinyint", "hugeint", "uint",
+    "varchar", "text", "char", "bpchar", "string", "uuid",
+)
+
+# Prefijos de physical_type que EXCLUYEN ser clave foranea: medidas en coma flotante
+# (float/double/decimal/numeric/real), temporales (date/time/timestamp/interval) y
+# boolean. Se comprueban ANTES que las senales positivas (la exclusion gana: una
+# columna numeric con physical DOUBLE es una medida, no una FK).
+_FK_PHYSICAL_EXCLUDE = (
+    "float", "double", "decimal", "numeric", "real",
+    "date", "time", "timestamp", "interval",
+    "bool",
+)
+
+
+def _fk_name_signal(name):
+    """Detecta el sufijo de clave foranea en el nombre y devuelve ``(stem, sufijo)``.
+
+    Reconoce ``<algo>_id`` (snake), ``<Algo>Id`` y ``<algo>ID`` (camel). NO reconoce
+    el ``id``/``Id``/``ID`` generico a secas (suele ser la PK propia de la tabla, no
+    una referencia). En camelCase la ``I`` mayuscula marca el limite de palabra, asi
+    que ``paid``/``valid``/``grid`` (``id`` en minuscula y sin separador) NO matchean.
+
+    El ``stem`` se devuelve en minusculas y sirve de ``ref_table_guess`` (la tabla a
+    la que probablemente apunta): ``customer_id`` -> ``"customer"``, ``AlbumId`` ->
+    ``"album"``, ``manager_staff_id`` -> ``"manager_staff"``. Devuelve ``None`` si no
+    hay senal de nombre.
+    """
+    if not isinstance(name, str):
+        return None
+    raw = name.strip()
+    if not raw:
+        return None
+    # Snake: termina en "_id" (indiferente a mayusculas en la parte "id").
+    if raw.lower().endswith("_id"):
+        stem = raw[:-3].rstrip("_-. ")
+        if not stem:
+            return None
+        return (stem.lower(), "_id")
+    # Camel todo-mayuscula: "...ID" (p.ej. customerID).
+    if raw.endswith("ID"):
+        stem = raw[:-2].rstrip("_-. ")
+        if not stem:
+            return None
+        return (stem.lower(), "ID")
+    # Camel: "...Id" (p.ej. AlbumId).
+    if raw.endswith("Id"):
+        stem = raw[:-2].rstrip("_-. ")
+        if not stem:
+            return None
+        return (stem.lower(), "Id")
+    return None
+
+
+def _fk_type_compatible(col):
+    """True si el tipo de la columna admite ser clave foranea.
+
+    Compatible si el ``physical_type`` NO es una medida flotante, una temporal ni
+    boolean, Y ademas (``inferred_type`` en {numeric, categorical, integer} O el
+    ``physical_type`` empieza por entero/varchar/text/char/uuid). La comparacion es
+    indistinta a mayusculas/minusculas.
+    """
+    phys = (col.get("physical_type") or "").strip().lower()
+    inferred = (col.get("inferred_type") or "").strip().lower()
+    # Exclusion por tipo fisico (gana sobre cualquier senal positiva).
+    for bad in _FK_PHYSICAL_EXCLUDE:
+        if phys.startswith(bad):
+            return False
+    # Senal positiva por tipo inferido.
+    if inferred in _FK_INFERRED_OK:
+        return True
+    # Senal positiva por tipo fisico (entero/texto/uuid).
+    for good in _FK_PHYSICAL_PREFIXES:
+        if phys.startswith(good):
+            return True
+    return False
+
+
+def suggest_intratable_fk_candidates(profile: dict, max_candidates: int = 20) -> list:
+    """Sugiere columnas que parecen una FK intra-tabla por nombre + cardinalidad.
+
+    Heuristica (no afirma nada): una columna es candidata a clave foranea si su nombre
+    tiene sufijo de id con stem no vacio (``<algo>_id`` / ``<Algo>Id`` / ``<algo>ID``,
+    NUNCA el ``id`` generico), no es ya candidata a PK, no es constante, tiene
+    cardinalidad alta pero por debajo del numero de filas (N:1, no unica) y un tipo
+    compatible con clave (entero/categorico/texto/uuid; nunca float/fecha/boolean).
+
+    Args:
+        profile: TableProfile (dict de ``profile_table``). Se leen, de forma
+            defensiva, ``columns`` (lista de ColumnProfile), ``n_rows`` y
+            ``key_candidates`` (nombres de columna ya candidatos a PK).
+        max_candidates: tope de sugerencias devueltas (default 20). Las columnas se
+            ordenan por ``distinct_count`` descendente (mas informativas primero)
+            antes de cortar.
+
+    Returns:
+        list de dicts (posiblemente vacia), uno por columna sugerida, con claves:
+        ``column``, ``ref_table_guess`` (stem del nombre), ``reason`` (frase humana),
+        ``distinct_count``, ``unique_pct`` (fraccion 0-1 tal como viene del profile),
+        ``inferred_type``, ``physical_type``. Nunca lanza: si ``profile`` no es dict o
+        no hay columnas, devuelve ``[]``.
+    """
+    if not isinstance(profile, dict):
+        return []
+    columns = profile.get("columns")
+    if not isinstance(columns, list):
+        return []
+
+    n_rows = profile.get("n_rows")
+    has_n_rows = (
+        isinstance(n_rows, int) and not isinstance(n_rows, bool) and n_rows > 0
+    )
+
+    key_candidates = profile.get("key_candidates")
+    if not isinstance(key_candidates, (list, tuple, set)):
+        key_candidates = []
+    key_set = set(key_candidates)
+
+    out = []
+    for col in columns:
+        if not isinstance(col, dict):
+            continue
+        name = col.get("name")
+
+        # 1) Senal de nombre: sufijo de id con stem no vacio.
+        signal = _fk_name_signal(name)
+        if signal is None:
+            continue
+        ref_guess, suffix = signal
+
+        # 2) No es ya candidata a PK (clave primaria de la propia tabla).
+        if name in key_set:
+            continue
+
+        # 3) No constante y con >= 2 valores distintos.
+        flags = col.get("flags") or []
+        if "constant" in flags:
+            continue
+        dc = col.get("distinct_count")
+        if not (isinstance(dc, int) and not isinstance(dc, bool) and dc >= 2):
+            continue
+
+        # 4) Cardinalidad alta pero < n_rows (no es PK) y no parece unica.
+        if has_n_rows and dc >= n_rows:
+            continue
+        unique_pct = col.get("unique_pct")
+        has_unique = (
+            isinstance(unique_pct, (int, float)) and not isinstance(unique_pct, bool)
+        )
+        if has_unique and unique_pct >= 0.99:
+            continue
+
+        # 5) Tipo compatible con clave foranea (entero/categorico/texto; no medida).
+        if not _fk_type_compatible(col):
+            continue
+
+        out.append(
+            {
+                "column": name,
+                "ref_table_guess": ref_guess,
+                "reason": _build_reason(suffix, dc, n_rows if has_n_rows else None, ref_guess),
+                "distinct_count": dc,
+                "unique_pct": float(unique_pct) if has_unique else None,
+                "inferred_type": col.get("inferred_type") or "",
+                "physical_type": col.get("physical_type") or "",
+            }
+        )
+
+    # Mas informativas primero (mayor cardinalidad), luego corte.
+    out.sort(key=lambda d: d.get("distinct_count") or 0, reverse=True)
+    return out[: max(0, int(max_candidates))]
+
+
+def _build_reason(suffix, dc, n_rows, ref_guess):
+    """Frase humana que deja claro que la sugerencia es heuristica, no confirmada."""
+    if n_rows is not None:
+        card = f"es N:1 ({dc} valores distintos < {n_rows} filas)"
+    else:
+        card = f"tiene {dc} valores distintos que se repiten (cardinalidad N:1)"
+    return (
+        f"el nombre termina en '{suffix}' y {card}: parece (heuristica por nombre, "
+        f"sin confirmar containment) una referencia a una tabla «{ref_guess}»"
+    )

python/functions/datascience/suggest_intratable_fk_candidates_test.py

@@ -0,0 +1,157 @@
+"""Tests para suggest_intratable_fk_candidates (funcion pura, sin I/O)."""
+
+from suggest_intratable_fk_candidates import suggest_intratable_fk_candidates
+
+
+def _col(name, inferred_type="numeric", physical_type="BIGINT", distinct_count=10,
+         unique_pct=0.1, flags=None):
+    """Construye un ColumnProfile minimo a mano (el dict que emite profile_table)."""
+    return {
+        "name": name,
+        "inferred_type": inferred_type,
+        "physical_type": physical_type,
+        "semantic_type": "",
+        "distinct_count": distinct_count,
+        "unique_pct": unique_pct,
+        "null_count": 0,
+        "null_pct": 0.0,
+        "flags": list(flags) if flags else [],
+    }
+
+
+def test_golden_customer_id_detectado_otras_no():
+    # Tabla tipo titanic: customer_id es FK N:1; id es la PK; amount es medida;
+    # name es categorica sin sufijo de id. Solo customer_id debe aparecer.
+    profile = {
+        "n_rows": 891,
+        "key_candidates": ["id"],
+        "columns": [
+            _col("id", inferred_type="numeric", physical_type="BIGINT",
+                 distinct_count=891, unique_pct=1.0, flags=["possible_id"]),
+            _col("customer_id", inferred_type="numeric", physical_type="BIGINT",
+                 distinct_count=137, unique_pct=0.15, flags=[]),
+            _col("amount", inferred_type="numeric", physical_type="DOUBLE",
+                 distinct_count=400, unique_pct=0.45),
+            _col("name", inferred_type="categorical", physical_type="VARCHAR",
+                 distinct_count=700, unique_pct=0.78),
+        ],
+    }
+    out = suggest_intratable_fk_candidates(profile)
+    assert isinstance(out, list)
+    assert [c["column"] for c in out] == ["customer_id"]
+    cand = out[0]
+    assert cand["ref_table_guess"] == "customer"
+    assert cand["distinct_count"] == 137
+    assert cand["unique_pct"] == 0.15
+    assert cand["inferred_type"] == "numeric"
+    assert cand["physical_type"] == "BIGINT"
+    # La razon deja claro que es heuristica + cita el sufijo y la tabla.
+    assert "customer" in cand["reason"]
+    assert "_id" in cand["reason"]
+
+
+def test_camelcase_albumid_detectado():
+    # AlbumId (camelCase, VARCHAR) -> detectada, ref_table_guess "album".
+    profile = {
+        "n_rows": 3503,
+        "key_candidates": ["TrackId"],
+        "columns": [
+            _col("AlbumId", inferred_type="categorical", physical_type="VARCHAR",
+                 distinct_count=347, unique_pct=0.10),
+        ],
+    }
+    out = suggest_intratable_fk_candidates(profile)
+    # TrackId es PK candidata (en key_candidates), AlbumId no -> AlbumId aparece.
+    assert [c["column"] for c in out] == ["AlbumId"]
+    assert out[0]["ref_table_guess"] == "album"
+
+
+def test_constante_status_id_no_aparece():
+    # status_id constante (flag "constant", distinct_count 1) NO es FK util.
+    profile = {
+        "n_rows": 1000,
+        "key_candidates": [],
+        "columns": [
+            _col("status_id", inferred_type="numeric", physical_type="INTEGER",
+                 distinct_count=1, unique_pct=0.001, flags=["constant"]),
+        ],
+    }
+    out = suggest_intratable_fk_candidates(profile)
+    assert out == []
+
+
+def test_profile_vacio_y_none_devuelven_lista_vacia():
+    # Lectura defensiva: ni {} ni None lanzan; devuelven [].
+    assert suggest_intratable_fk_candidates({}) == []
+    assert suggest_intratable_fk_candidates(None) == []
+    # profile sin columns o con columns no-lista tampoco lanza.
+    assert suggest_intratable_fk_candidates({"n_rows": 10}) == []
+    assert suggest_intratable_fk_candidates({"columns": "no-soy-lista"}) == []
+
+
+def test_category_id_casi_unico_parece_pk_no_aparece():
+    # unique_pct 0.999 -> parece PK (no N:1) -> NO se sugiere como FK.
+    profile = {
+        "n_rows": 891,
+        "key_candidates": [],
+        "columns": [
+            _col("category_id", inferred_type="numeric", physical_type="BIGINT",
+                 distinct_count=890, unique_pct=0.999),
+        ],
+    }
+    out = suggest_intratable_fk_candidates(profile)
+    assert out == []
+
+
+def test_ref_table_guess_multitoken_y_orden_por_distinct():
+    # manager_staff_id conserva los underscores del stem -> "manager_staff".
+    # Ademas, con varias candidatas, se ordenan por distinct_count descendente.
+    profile = {
+        "n_rows": 10000,
+        "key_candidates": ["staff_id"],  # staff_id es PK aqui, no debe aparecer
+        "columns": [
+            _col("staff_id", inferred_type="numeric", physical_type="BIGINT",
+                 distinct_count=10000, unique_pct=1.0, flags=["possible_id"]),
+            _col("store_id", inferred_type="numeric", physical_type="INTEGER",
+                 distinct_count=2, unique_pct=0.0002),
+            _col("manager_staff_id", inferred_type="numeric", physical_type="INTEGER",
+                 distinct_count=40, unique_pct=0.004),
+        ],
+    }
+    out = suggest_intratable_fk_candidates(profile)
+    cols = [c["column"] for c in out]
+    # staff_id excluida (PK); las otras dos ordenadas por distinct desc.
+    assert cols == ["manager_staff_id", "store_id"]
+    refs = {c["column"]: c["ref_table_guess"] for c in out}
+    assert refs["manager_staff_id"] == "manager_staff"
+    assert refs["store_id"] == "store"
+
+
+def test_max_candidates_corta_la_lista():
+    # max_candidates limita el numero de sugerencias devueltas.
+    profile = {
+        "n_rows": 10000,
+        "key_candidates": [],
+        "columns": [
+            _col("a_id", distinct_count=300, unique_pct=0.03),
+            _col("b_id", distinct_count=200, unique_pct=0.02),
+            _col("c_id", distinct_count=100, unique_pct=0.01),
+        ],
+    }
+    out = suggest_intratable_fk_candidates(profile, max_candidates=2)
+    assert [c["column"] for c in out] == ["a_id", "b_id"]
+
+
+def test_id_generico_solo_nunca_es_fk():
+    # 'id'/'Id'/'ID' a secas (sin stem) jamas se sugieren como FK.
+    profile = {
+        "n_rows": 500,
+        "key_candidates": [],
+        "columns": [
+            _col("id", distinct_count=500, unique_pct=1.0),
+            _col("Id", distinct_count=120, unique_pct=0.24),
+            _col("ID", distinct_count=80, unique_pct=0.16),
+        ],
+    }
+    out = suggest_intratable_fk_candidates(profile)
+    assert out == []

python/functions/datascience/summarize_table_duckdb.md

@@ -3,7 +3,7 @@ name: summarize_table_duckdb
 kind: function
 lang: py
 domain: datascience
-version: "1.0.0"
+version: "1.1.0"
 purity: impure
 signature: "def summarize_table_duckdb(db_path: str, table: str, high_card_ratio: float = 0.9) -> dict"
 description: "Perfila una tabla DuckDB en una sola pasada SQL (SUMMARIZE, push-down sin traer filas a RAM) y devuelve el esqueleto de un TableProfile con el perfil base por columna. Corazon del grupo eda: base barata sobre la que otras funciones anaden lo estadistico fino (skew/kurtosis/histograma sobre muestra)."
@@ -64,6 +64,7 @@ else:
 - **`distinct_count` exacto para tablas <=200k filas, aproximado+capado por encima**: `SUMMARIZE` usa HyperLogLog (`approx_unique`), que SOBREESTIMA y en tablas pequenas puede reportar mas distintos que filas (inflando `unique_pct` por encima de 1.0 y disparando flags `possible_id` falsos). Por eso, para `n_rows <= 200000` la funcion calcula `COUNT(DISTINCT)` EXACTO en una sola query combinada (barata) y usa ese valor. Para tablas mas grandes mantiene `approx_unique` pero lo CAPA a `n_rows` (`distinct_count = min(approx_unique, n_rows)`). En ambos casos `unique_pct = min(distinct_count / n_rows, 1.0)`, asi que `distinct_count` nunca supera las filas ni `unique_pct` pasa de 1.0. Los flags `possible_id` / `high_cardinality` derivan de ese `distinct_count` ya corregido (exacto y fiable por debajo de 200k filas; aproximado y conservador por encima).
 - **`SUMMARIZE` NO da skew, kurtosis ni histograma**, ni percentiles finos (p1/p5/p95/p99), moda, outliers, correlaciones, key_candidates ni quality_score. Esas claves quedan en `None`/`[]` a proposito: las rellena otra funcion del grupo `eda` sobre una muestra. El sub-dict `numeric` solo trae min, max, mean, std, p25, p50, p75.
 - **`SUMMARIZE.count` es el total de filas, no el no-nulo**: la funcion deriva el `count` no-nulo del ColumnProfile como `n_rows - null_count` (con `null_count` redondeado de `null_percentage`).
+- **`duplicate_rows`/`duplicate_pct` se pueblan push-down** (desde v1.1.0) con `count(*)` sobre `SELECT DISTINCT *` (sin traer filas a RAM): `duplicate_rows = n_rows - filas_distintas`, `duplicate_pct` en fraccion 0-1. Habilitan la dimension de unicidad de registro del score de dataset (`profile_table` paso 6). Si la tabla tiene tipos no comparables con `DISTINCT` (BLOB/LIST/MAP) la query degrada y ambas vuelven a `None` (renormaliza el score a solo `cell_quality`).
 - **min/max/avg/std/q25/q50/q75 vienen como strings** desde DuckDB; se convierten a float (None si la columna no es numerica).
 - **Requiere DuckDB 1.5.2** (columnas de `SUMMARIZE` validadas con esa version: column_name, column_type, min, max, approx_unique, avg, std, q25, q50, q75, count, null_percentage).
 - **El identificador de tabla se interpola** (no parametrizable en `SUMMARIZE`): por eso se valida contra `^[A-Za-z_][A-Za-z0-9_]*$` antes de citarlo. Un nombre invalido (p.ej. con `;` o espacios) devuelve `{status:'error'}` sin tocar la base.

python/functions/datascience/summarize_table_duckdb.py

@@ -196,6 +196,21 @@ def summarize_table_duckdb(
             sum(c["null_pct"] for c in columns) / len(columns) if columns else 0.0
         )
 
+        # Unicidad de registro: filas duplicadas via COUNT de filas distintas
+        # push-down (DISTINCT *), sin traer filas a RAM. Habilita la dimension
+        # de uniqueness del score de dataset (1 - duplicate_pct). Degrada a None
+        # si la tabla tiene tipos no comparables con DISTINCT (BLOB/LIST/MAP).
+        duplicate_rows = None
+        duplicate_pct = None
+        if n_rows > 0:
+            dup_res = duckdb_query_readonly(
+                db_path, f"SELECT count(*) AS c FROM (SELECT DISTINCT * FROM {quoted})"
+            )
+            if dup_res["status"] == "ok" and dup_res["rows"]:
+                distinct_rows = int(dup_res["rows"][0]["c"])
+                duplicate_rows = max(0, n_rows - distinct_rows)
+                duplicate_pct = duplicate_rows / n_rows  # fraccion 0-1
+
         profile = {
             "table": table,
             "source": "duckdb",
@@ -203,8 +218,8 @@ def summarize_table_duckdb(
             "n_rows": n_rows,
             "n_cols": len(columns),
             "size_bytes": None,
-            "duplicate_rows": None,
+            "duplicate_rows": duplicate_rows,
-            "duplicate_pct": None,
+            "duplicate_pct": duplicate_pct,
             "constant_cols": constant_cols,
             "all_null_cols": all_null_cols,
             "null_cell_pct": null_cell_pct,

python/functions/datascience/summarize_table_duckdb_test.py

@@ -54,6 +54,30 @@ def test_shape_y_metadatos_tabla(db):
     assert profile["correlations"] is None
 
 
+def test_duplicate_pct_sin_duplicados(db):
+    """Tabla con todas las filas distintas: duplicate_pct = 0, no None."""
+    profile = summarize_table_duckdb(db, "ventas")["profile"]
+    assert profile["duplicate_rows"] == 0
+    assert profile["duplicate_pct"] == 0.0
+
+
+def test_duplicate_pct_con_duplicados(tmp_path):
+    """Filas repetidas: duplicate_rows/duplicate_pct se pueblan push-down."""
+    path = str(tmp_path / "dups.duckdb")
+    con = duckdb.connect(path)
+    con.execute("CREATE TABLE t (a INTEGER, b VARCHAR)")
+    # 5 filas, 2 de ellas idénticas a otras -> 2 duplicadas sobre 5 = 0.4.
+    con.execute(
+        "INSERT INTO t VALUES "
+        "(1,'x'), (2,'y'), (1,'x'), (3,'z'), (2,'y')"
+    )
+    con.close()
+    profile = summarize_table_duckdb(path, "t")["profile"]
+    assert profile["n_rows"] == 5
+    assert profile["duplicate_rows"] == 2
+    assert profile["duplicate_pct"] == 0.4
+
+
 def test_column_profile_shape(db):
     profile = summarize_table_duckdb(db, "ventas")["profile"]
     by_name = {c["name"]: c for c in profile["columns"]}

python/functions/pipelines/profile_table.md

@@ -4,7 +4,7 @@ kind: pipeline
 lang: py
 domain: pipelines
 purity: impure
-version: "1.0.0"
+version: "1.1.0"
 signature: "def profile_table(db_path: str, table: str, backend: str = \"duckdb\", sample: int = 5000, run_models: bool = False, run_llm: bool = False, run_series: bool = False, emit_pdf: bool = False, emit_automatic: bool = False, report_dir: str = \"reports\", write_report: bool = True) -> dict"
 description: "Orquestador one-shot del grupo de capacidad eda: perfila UNA tabla (DuckDB o PostgreSQL) end-to-end componiendo las funciones del grupo (perfil base SQL + muestreo read-only + inferencia semantica + promocion de tipo + estadistica numerica/categorica + score de calidad + correlaciones con correccion FDR + re-expresion de Tukey + avisos exploratorios) y, opcional, modelos baratos (run_models), interpretacion LLM (run_llm) y analisis de serie temporal por columna (run_series: estacionariedad ADF+KPSS, ACF/PACF, STL, retornos). Emite el TableProfile completo mas (opcional) report markdown + JSON sidecar + PDF movil (emit_pdf). Es la composicion canonica para hazme un EDA de esta tabla."
 tags: [eda, duckdb, postgres, profiling, data-quality, pipeline, dataops, timeseries]
@@ -114,3 +114,12 @@ para auditar la calidad de una tabla ya productiva. Reemplaza orquestar a mano
   Formatos exoticos pueden descartarse silenciosamente del calculo numerico.
 - `db_path` debe existir: DuckDB read-only NO crea la base. El muestreo usa el
   sandbox por defecto de `duckdb_query_readonly` (sin acceso a FS/red).
+- **Score de calidad (report 2046, desde v1.1.0).** Paso 5: cada columna recibe
+  `quality_score` de `column_quality_score` con la formula 60/40
+  (completeness/validity); al promocionar texto a numero/fecha se expone
+  `col["validity_rate"]` (parse rate de la muestra) para alimentar la dimension
+  validity. Paso 6: el score de dataset NO es la media simple — es
+  `100 * (0.85*cell_quality + 0.15*row_uniqueness)`, donde
+  `cell_quality = media(score_col/100)` y `row_uniqueness = 1 - duplicate_pct`.
+  Si `duplicate_pct` es `None` (backend sin calcularlo) el score se renormaliza
+  a solo `cell_quality`. Los outliers NO bajan el score (van a `observations`).

python/functions/pipelines/profile_table.py

@@ -477,9 +477,18 @@ def profile_table(
                     if vals and (len(ok) / len(vals)) >= _PROMOTE_MIN_PARSE:
                         col["inferred_type"] = "numeric"
                         inferred = "numeric"
+                        # Tasa de parseo real de la muestra: alimenta la
+                        # dimension validity de column_quality_score (fraccion
+                        # de valores conformes al tipo numerico promovido).
+                        col["validity_rate"] = len(ok) / len(vals)
                 elif semantic in _DATETIME_SEMANTIC:
                     col["inferred_type"] = "datetime"
                     inferred = "datetime"
+                    # Tasa de parseo de la muestra a fecha (mismo papel que el
+                    # parse rate numerico) para la dimension validity.
+                    parsed_dt = [_to_ordinal_days(v) for v in vals]
+                    ok_dt = [d for d in parsed_dt if d is not None]
+                    col["validity_rate"] = (len(ok_dt) / len(vals)) if vals else None
 
             # 4) Enriquecer segun el inferred_type final.
             if inferred == "numeric":
@@ -506,11 +515,36 @@ def profile_table(
             # 5) Score de calidad por columna.
             col["quality_score"] = column_quality_score(col).get("score")
 
-        # 6) Score agregado de la tabla (media de columnas).
+        # 6) Score agregado de la tabla (report 2046): NO media simple.
+        #   cell_quality   = media de los scores de columna, en [0,1].
+        #   row_uniqueness = 1 - duplicate_pct (unicidad de registro).
+        #   score = 100 * (0.85*cell_quality + 0.15*row_uniqueness).
+        # Renormaliza a solo cell_quality si duplicate_pct no se pudo calcular.
         scores = [
             c["quality_score"] for c in cols if c.get("quality_score") is not None
         ]
-        prof["quality_score"] = round(sum(scores) / len(scores), 1) if scores else None
+        if scores:
+            cell_quality = (sum(scores) / len(scores)) / 100.0
+            dup_pct = prof.get("duplicate_pct")
+            if dup_pct is not None:
+                try:
+                    d = float(dup_pct)
+                except (TypeError, ValueError):
+                    d = None
+            else:
+                d = None
+            if d is not None:
+                # Tolerar escala 0-100 por si algun backend la entrega asi.
+                if d > 1.0:
+                    d = d / 100.0
+                row_uniqueness = max(0.0, min(1.0, 1.0 - d))
+                prof["quality_score"] = round(
+                    100.0 * (0.85 * cell_quality + 0.15 * row_uniqueness), 1
+                )
+            else:
+                prof["quality_score"] = round(100.0 * cell_quality, 1)
+        else:
+            prof["quality_score"] = None
 
         # 7) Candidatos a clave.
         key_candidates = []
@@ -536,6 +570,21 @@ def profile_table(
                 type_breakdown[it] += 1
         prof["type_breakdown"] = type_breakdown
 
+        # 8.1) Primeras filas crudas (df.head) para el capitulo OVERVIEW del motor
+        # AutomaticEDA: una muestra SELECT col1,col2,... LIMIT 10 alineada por fila.
+        # Se reusa _sample_rows (mismo lector read-only). Estilo dict-no-throw: si
+        # falla, head_rows queda None y el capitulo degrada a su nota honesta. El
+        # capitulo lo recoge via profile["head_rows"]; build_eda_render_ctx ademas
+        # lo replica en ctx["head_rows"] cuando se construye el contexto de render.
+        try:
+            head_names = [c.get("name") for c in cols if c.get("name")]
+            head_rows = _sample_rows(_q, table, head_names, 10)
+            prof["head_rows"] = [
+                dict(r) for r in head_rows if isinstance(r, dict)
+            ] or None
+        except Exception:  # noqa: BLE001
+            prof["head_rows"] = None
+
         # 8.5) Matriz de correlacion/asociacion sobre una muestra de filas
         # alineadas. Elige la metrica por par de tipos (Pearson/Spearman,
         # Cramer's V/Theil's U, correlation ratio, MI) via association_matrix.

python/functions/pipelines/render_automatic_eda.md

@@ -4,9 +4,9 @@ kind: pipeline
 lang: py
 domain: pipelines
 purity: impure
-version: "1.0.0"
+version: "1.1.0"
-signature: "def render_automatic_eda(db_path: str, table: str, backend: str = \"duckdb\", sample: int = 5000, run_models: bool = True, run_series: bool = True, run_llm: bool = False, out_dir: str = \"reports\", basename: str = None, ctx_extra: dict = None) -> dict"
+signature: "def render_automatic_eda(db_path: str, table: str, backend: str = \"duckdb\", sample: int = None, run_models: bool = None, run_series: bool = None, run_llm: bool = None, profile_level: str = \"standard\", out_dir: str = \"reports\", basename: str = None, ctx_extra: dict = None) -> dict"
-description: "Informe AutomaticEDA COMPLETO one-shot de una tabla DuckDB/PostgreSQL: perfila con profile_table, construye el ctx con los datos crudos (build_eda_render_ctx: raw_numeric para modelos/geo, timeseries_raw para series, geo_points para el mapa, db_path/table para la agregacion push-down) y emite PDF (A5 movil) Y PPTX (16:9) del mismo documento por capitulos, con los 11 capitulos POBLADOS de verdad (clusters pintados sobre el PCA, evolucion temporal, mapa geografico y tablas de agregacion), no degradados. Devuelve las rutas de PDF/PPTX y el manifiesto de versiones por capitulo."
+description: "Informe AutomaticEDA COMPLETO one-shot de una tabla DuckDB/PostgreSQL: perfila con profile_table, construye el ctx con los datos crudos (build_eda_render_ctx: raw_numeric para modelos/geo, timeseries_raw para series, geo_points para el mapa, db_path/table para la agregacion push-down) y emite PDF (A5 movil) Y PPTX (16:9) del mismo documento por capitulos, con los 11 capitulos POBLADOS de verdad (clusters pintados sobre el PCA, evolucion temporal, mapa geografico y tablas de agregacion), no degradados. El parametro profile_level es un preset de consumo CPU/LLM (lite/standard/full) que mapea a los flags run_models/run_series/run_llm/sample; un flag explicito siempre prima sobre el preset. lite=bajo consumo (sin LLM, sin serie, modelos solo PCA+normalidad sin KMeans/IsolationForest, sample reducido); standard=comportamiento historico; full=standard+narrativa LLM. Devuelve las rutas de PDF/PPTX y el manifiesto de versiones por capitulo."
 tags: [eda, duckdb, postgres, profiling, pipeline, dataops, report, pdf, pptx]
 uses_functions:
   - profile_table_py_pipelines
@@ -31,13 +31,15 @@ params:
   - name: backend
     desc: "'duckdb' (default) o 'postgres'. Selecciona el motor de perfilado y muestreo."
   - name: sample
-    desc: "Maximo de filas/valores muestreados por columna para el perfil y para los datos crudos del ctx (LIMIT). Default 5000."
+    desc: "Maximo de filas/valores muestreados por columna para el perfil y para los datos crudos del ctx (LIMIT). Default None => lo fija el preset de profile_level (lite=2000, standard/full=5000). Un valor explicito prima sobre el preset."
   - name: run_models
-    desc: "Si True (default) corre los modelos baratos (PCA/KMeans/IsolationForest/normalidad); necesario para que el capitulo modelos pinte los clusters sobre el plano PCA."
+    desc: "Corre los modelos baratos (PCA/KMeans/IsolationForest/normalidad); necesario para que el capitulo modelos pinte los clusters sobre el plano PCA. Default None => lo fija el preset (True en los tres niveles); en lite los modelos se limitan a PCA+normalidad. Un valor explicito prima sobre el preset."
   - name: run_series
-    desc: "Si True (default) calcula el analisis de serie temporal por columna numerica; necesario para el analisis del capitulo timeseries (la grafica de evolucion sale de los datos crudos del ctx aunque sea False)."
+    desc: "Calcula el analisis de serie temporal por columna numerica; necesario para el analisis del capitulo timeseries. Default None => lo fija el preset (standard/full=True, lite=False). Un valor explicito prima sobre el preset."
   - name: run_llm
-    desc: "Si True (default False) hace la interpretacion LLM del perfil y ACTIVA la narrativa LLM de los capitulos modelos/geospatial/agregacion (titulos de segmento, descripcion de zona, seleccion de agregaciones). Con False usan su derivacion cuantitativa sin red."
+    desc: "Hace la interpretacion LLM del perfil y ACTIVA la narrativa LLM de los capitulos modelos/geospatial/agregacion (titulos de segmento, descripcion de zona, seleccion de agregaciones). Con False usan su derivacion cuantitativa sin red. Default None => lo fija el preset (full=True, lite/standard=False). Un valor explicito prima sobre el preset."
+  - name: profile_level
+    desc: "Preset de consumo CPU/LLM (default 'standard'). Mapea a defaults de run_models/run_series/run_llm/sample; un flag explicito SIEMPRE prima. 'lite'=bajo consumo (run_llm=False, run_series=False, sample=2000, modelos solo PCA+normalidad sin KMeans/IsolationForest); 'standard'=comportamiento historico (modelos completos, serie, sin LLM); 'full'=standard+narrativa LLM. Un nivel desconocido cae a 'standard'."
   - name: out_dir
     desc: "Directorio de salida (se crea si no existe). Default 'reports'."
   - name: basename
@@ -52,14 +54,21 @@ output: "dict {status:'ok', pdf_path:str, pptx_path:str, manifest_path:str|None,
 ```python
 from pipelines.render_automatic_eda import render_automatic_eda
 
-# Tabla DuckDB con categoricas + fecha + numericas: informe completo a reports/.
+# Informe completo a reports/ (standard = comportamiento por defecto historico).
-r = render_automatic_eda("/tmp/ventas.duckdb", "ventas",
+r = render_automatic_eda("/tmp/ventas.duckdb", "ventas", out_dir="reports")
-                         run_models=True, run_series=True, out_dir="reports")
 print(r["status"], r["pdf_path"], r["pptx_path"], r["n_pages"], r["n_slides"])
-# ok reports/aeda_ventas_20260630-120500.pdf reports/aeda_ventas_20260630-120500.pptx 14 16
+# ok reports/aeda_ventas_20260630-120500.pdf reports/aeda_ventas_20260630-120500.pptx 37 39
 
-# Con narrativa LLM (titulos de segmento, descripcion geografica, etc.):
+# Bajo consumo (CPU/LLM): vistazo rapido y barato — sin LLM, sin serie, modelos
-r = render_automatic_eda("/tmp/ventas.duckdb", "ventas", run_llm=True)
+# solo PCA + normalidad (sin KMeans/IsolationForest), sample reducido.
+r = render_automatic_eda("/tmp/ventas.duckdb", "ventas", profile_level="lite")
+
+# Maximo: standard + narrativa LLM por capitulo (titulos de segmento, etc.).
+r = render_automatic_eda("/tmp/ventas.duckdb", "ventas", profile_level="full")
+
+# Precedencia: el flag explicito SIEMPRE prima sobre el preset. lite pero con LLM:
+r = render_automatic_eda("/tmp/ventas.duckdb", "ventas",
+                         profile_level="lite", run_llm=True)  # el LLM SI se ejecuta
 ```
 
 ## Cuando usarla
@@ -72,20 +81,41 @@ llama a los dos renderers": este pipeline orquesta `profile_table` ->
 entregable para compartir un EDA, o como el motor detras de `profile_table(
 emit_automatic=True)` y del skill `/eda`.
 
+Para un EDA **barato/rapido** (CI, vistazo previo, maquina sin GPU o sin red) usa
+`profile_level="lite"`: evita KMeans + IsolationForest (lo caro en CPU), la serie
+temporal y el LLM. Para el **maximo** con interpretacion narrativa por capitulo,
+`profile_level="full"`. El default `"standard"` mantiene el comportamiento previo.
+
 ## Gotchas
 
 - Impura: ESCRIBE el PDF, el PPTX y `automatic_eda_manifest.json` en `out_dir`.
 - `db_path` debe existir: DuckDB read-only no crea la base.
-- `run_models=True` y `run_series=True` por defecto encarecen el perfil (PCA/
+- **Precedencia de flags vs preset**: `profile_level` solo fija los DEFAULTS de
-  KMeans/IsolationForest + ADF/KPSS/STL por columna). Para un informe mas barato
+  `run_models`/`run_series`/`run_llm`/`sample` (los que quedan en None). Cualquiera
-  ponlos a False: los capitulos modelos/timeseries se omiten o se reducen, pero
+  de esos flags pasado explicito gana al preset. Ej: `profile_level="lite",
-  el resto del informe sale igual.
+  run_llm=True` ejecuta el LLM pese a que lite lo apaga por defecto.
-- `run_llm=True` hace llamadas de red (interpretacion del perfil + narrativa por
+- **lite y la seleccion de features de modelo**: en lite los modelos (PCA +
-  capitulo). Sin red, dejalo en False: los capitulos siguen completos con su
+  normalidad) corren sobre la muestra numerica cruda (`ctx['raw_numeric']`), sin la
-  derivacion cuantitativa (titulos de segmento derivados, nota geografica
+  poda fina de features que aplica el modo standard (que excluye ids enteros y
-  derivada, seleccion de agregaciones cuantitativa).
+  columnas de baja cardinalidad antes de PCA/KMeans). Es el coste de mantener el
+  cableado minimo y barato; para el analisis fino de modelos usa standard/full.
+- `profile_level="standard"`/`"full"` corren PCA/KMeans/IsolationForest +
+  ADF/KPSS/STL por columna (caro). Para un informe mas barato usa `"lite"` (o pon
+  los flags a False a mano): los capitulos modelos/timeseries se reducen pero el
+  resto del informe sale igual.
+- `run_llm=True` (preset full o flag explicito) hace llamadas de red
+  (interpretacion del perfil + narrativa por capitulo). Sin red, usa lite/standard:
+  los capitulos siguen completos con su derivacion cuantitativa.
 - El PPTX requiere `python-pptx`; si no esta instalado, `pptx_path` sera None y
   `pptx_note` lo explica (el PDF se emite igual).
 - Los datos crudos del ctx se muestrean con `sample` (LIMIT), no se trae la tabla
   entera a RAM; con tablas enormes sube `sample` si quieres mas representatividad
   (coste: mas memoria).
+
+## Capability growth log
+
+- v1.1.0 (2026-06-30) — anade el parametro `profile_level` (lite/standard/full),
+  preset de consumo CPU/LLM que mapea a los flags run_models/run_series/run_llm/
+  sample. lite limita los modelos a PCA+normalidad (cableado a run_eda_models con
+  run_kmeans=False/run_isolation=False) y apaga LLM/serie. Cambio aditivo y
+  retro-compatible: sin profile_level el comportamiento es identico al de v1.0.0.

python/functions/pipelines/render_automatic_eda.py

@@ -34,21 +34,62 @@ from datascience import (
     build_eda_render_ctx,
     render_automatic_eda_pdf,
     render_automatic_eda_pptx,
+    run_eda_models,
 )
 from pipelines.profile_table import profile_table
 
 # Tokens de almacenamiento por backend (para la portada del informe).
 _STORAGE = {"duckdb": "DuckDB", "postgres": "PostgreSQL"}
 
+# Presets de consumo CPU/LLM: cada profile_level fija SOLO los DEFAULTS de los
+# flags que controlan el coste (un flag explícito del caller siempre prima sobre
+# el preset). model_opts != None marca el camino "modelos baratos" (lite): los
+# modelos NO los corre profile_table (que ejecutaría KMeans + IsolationForest),
+# sino run_eda_models con esa granularidad, de modo que el coste CPU de los
+# multivariantes nunca se paga. model_opts None => modelos completos como hasta
+# ahora (profile_table los corre con todos los algoritmos).
+_PROFILE_PRESETS = {
+    # Bajo consumo: sin LLM, sin serie, sample reducido y modelos limitados a
+    # PCA + normalidad (sin KMeans ni IsolationForest, lo caro en CPU). Vistazo
+    # rápido y barato de una tabla.
+    "lite": {
+        "run_models": True,
+        "run_series": False,
+        "run_llm": False,
+        "sample": 2000,
+        "model_opts": {"run_kmeans": False, "run_isolation": False},
+    },
+    # Default: idéntico al comportamiento histórico del pipeline (modelos
+    # completos, serie temporal, sin LLM, sample 5000).
+    "standard": {
+        "run_models": True,
+        "run_series": True,
+        "run_llm": False,
+        "sample": 5000,
+        "model_opts": None,
+    },
+    # Máximo: standard + narrativa LLM (interpretación del perfil y de los
+    # capítulos modelos/geospatial/agregacion). Es la única parte que gasta
+    # tokens del modelo.
+    "full": {
+        "run_models": True,
+        "run_series": True,
+        "run_llm": True,
+        "sample": 5000,
+        "model_opts": None,
+    },
+}
+
 
 def render_automatic_eda(
     db_path: str,
     table: str,
     backend: str = "duckdb",
-    sample: int = 5000,
+    sample: int = None,
-    run_models: bool = True,
+    run_models: bool = None,
-    run_series: bool = True,
+    run_series: bool = None,
-    run_llm: bool = False,
+    run_llm: bool = None,
+    profile_level: str = "standard",
     out_dir: str = "reports",
     basename: str = None,
     ctx_extra: dict = None,
@@ -60,19 +101,39 @@ def render_automatic_eda(
         table: nombre de la tabla a perfilar.
         backend: "duckdb" (default) o "postgres".
         sample: máximo de filas/valores muestreados por columna para el perfil
-            y para los datos crudos del ctx (LIMIT). Default 5000.
+            y para los datos crudos del ctx (LIMIT). Default None => lo fija el
-        run_models: si True (default) corre los modelos baratos
+            preset de profile_level (lite=2000, standard/full=5000).
+        run_models: corre los modelos baratos
             (PCA/KMeans/IsolationForest/normalidad). Necesario para que el
-            capítulo `modelos` pinte los clusters sobre el plano PCA.
+            capítulo `modelos` pinte los clusters sobre el plano PCA. Default
-        run_series: si True (default) calcula el análisis de serie temporal por
+            None => lo fija el preset (True en los tres niveles); en `lite` los
+            modelos se limitan a PCA + normalidad (ver profile_level).
+        run_series: calcula el análisis de serie temporal por
             columna numérica. Necesario para el análisis del capítulo
             `timeseries` (la gráfica de evolución sale de los datos crudos del
-            ctx aunque run_series sea False).
+            ctx aunque run_series sea False). Default None => lo fija el preset
-        run_llm: si True (default False) hace la interpretación LLM del perfil y
+            (standard/full=True, lite=False).
+        run_llm: hace la interpretación LLM del perfil y
             ACTIVA además la narrativa LLM de los capítulos modelos/geospatial/
             agregacion (títulos de segmento, descripción de la zona, selección de
             agregaciones). Con False esos capítulos usan su derivación
-            cuantitativa (siguen completos, sin llamadas de red).
+            cuantitativa (siguen completos, sin llamadas de red). Default None =>
+            lo fija el preset (full=True, lite/standard=False).
+        profile_level: preset de consumo CPU/LLM. Mapea a defaults de los flags
+            anteriores; un flag explícito SIEMPRE prima sobre el preset (el
+            preset solo fija el default cuando el flag se deja en None):
+
+            - "lite"     bajo consumo: run_llm=False, run_series=False,
+              sample=2000 y modelos limitados a **PCA + normalidad** (SIN KMeans
+              ni IsolationForest, que es lo caro en CPU). Pensado para un vistazo
+              rápido y barato. El capítulo `modelos` sale con PCA + normalidad,
+              sin el scatter de clusters.
+            - "standard" (default): comportamiento histórico — modelos completos
+              (PCA/KMeans/IsolationForest/normalidad), serie temporal, sin LLM.
+            - "full"     standard + narrativa LLM (run_llm=True).
+
+            Ejemplo de precedencia: profile_level="lite" con run_llm=True
+            explícito => el LLM SÍ se ejecuta (el flag explícito gana al preset).
         out_dir: directorio de salida (se crea si no existe). Default "reports".
         basename: nombre base de los archivos sin extensión. Default
             "aeda_<table>_<timestamp>".
@@ -90,6 +151,24 @@ def render_automatic_eda(
         En error: {"status": "error", "error": str}.
     """
     try:
+        # 0) Resolución del preset: el profile_level fija los DEFAULTS de los
+        # flags de coste; cualquier flag que el caller haya pasado explícito
+        # (!= None) prima sobre el preset. Un profile_level desconocido cae a
+        # "standard" (comportamiento histórico), sin lanzar.
+        preset = _PROFILE_PRESETS.get(profile_level, _PROFILE_PRESETS["standard"])
+        sample = preset["sample"] if sample is None else sample
+        run_models = preset["run_models"] if run_models is None else run_models
+        run_series = preset["run_series"] if run_series is None else run_series
+        run_llm = preset["run_llm"] if run_llm is None else run_llm
+        model_opts = preset["model_opts"]
+
+        # En el camino "modelos baratos" (lite) profile_table NO corre los
+        # modelos: los ejecuta este pipeline con run_eda_models y la granularidad
+        # del preset, evitando pagar el coste CPU de KMeans + IsolationForest.
+        # En standard/full profile_table los corre completos como siempre.
+        lite_models = bool(run_models) and model_opts is not None
+        pt_run_models = bool(run_models) and not lite_models
+
         # 1) Perfil base + modelos/serie opcionales. No escribe report propio
         # (write_report=False): este pipeline emite su propio par PDF/PPTX.
         pres = profile_table(
@@ -97,7 +176,7 @@ def render_automatic_eda(
             table,
             backend=backend,
             sample=sample,
-            run_models=run_models,
+            run_models=pt_run_models,
             run_llm=run_llm,
             run_series=run_series,
             emit_pdf=False,
@@ -131,6 +210,28 @@ def render_automatic_eda(
             base_ctx=base_ctx,
         )
 
+        # 2.5) Camino lite — modelos baratos (PCA + normalidad, sin KMeans ni
+        # IsolationForest). profile_table no corrió los modelos; aquí se corren
+        # con run_eda_models reusando la muestra numérica alineada por fila que
+        # build_eda_render_ctx ya trajo en ctx['raw_numeric'] (no se reimplementa
+        # la lógica de los modelos: se delega en run_eda_models con la
+        # granularidad del preset).
+        if lite_models:
+            raw_numeric = ctx.get("raw_numeric") if isinstance(ctx, dict) else None
+            if isinstance(raw_numeric, dict) and raw_numeric:
+                model_input = {
+                    col: {"values": vals, "type": "numeric"}
+                    for col, vals in raw_numeric.items()
+                }
+                prof["models"] = run_eda_models(model_input, **model_opts)
+            # Quita raw_numeric del ctx para que el capítulo `modelos` NO
+            # reproyecte clusters KMeans en vivo (project_clusters_2d ejecuta
+            # KMeans): en lite ese coste se evita. geo_points ya quedó derivado
+            # en ctx por build_eda_render_ctx, así que el capítulo geospatial no
+            # se ve afectado.
+            if isinstance(ctx, dict):
+                ctx.pop("raw_numeric", None)
+
         # 3) Render a ambos formatos desde el MISMO documento por capítulos.
         os.makedirs(out_dir, exist_ok=True)
         ts = datetime.now(timezone.utc).strftime("%Y%m%d-%H%M%S")

python/functions/pipelines/render_automatic_eda_test.py

@@ -89,3 +89,170 @@ def test_pipeline_bad_db_degrades_without_raising(tmp_path):
                              out_dir=str(tmp_path / "o"))
     assert r["status"] == "error"
     assert "error" in r
+
+
+# --------------------------------------------------------------------------- #
+# profile_level: preset de bajo consumo CPU/LLM.
+# --------------------------------------------------------------------------- #
+def _make_db_models(path):
+    """DB con >=2 numéricas continuas (alta cardinalidad, 3 clusters gaussianos).
+
+    El DB `sales` de _make_db solo deja UNA columna de modelo tras la selección de
+    features (units es baja cardinalidad, lat/lon discretizadas), insuficiente para
+    PCA/KMeans/IsolationForest (necesitan >=2). Este DB sí tiene 3 numéricas
+    continuas con estructura de clusters para que el modo completo ejecute los
+    multivariantes.
+    """
+    import random
+    from datetime import date, timedelta
+
+    con = duckdb.connect(path)
+    con.execute(
+        "CREATE TABLE pts (d DATE, grp VARCHAR, x1 DOUBLE, x2 DOUBLE, x3 DOUBLE)"
+    )
+    random.seed(42)
+    centers = [(0.0, 0.0, 0.0), (10.0, 10.0, 10.0), (20.0, 5.0, 15.0)]
+    d0 = date(2024, 1, 1)
+    rows = []
+    for i in range(150):
+        cx, cy, cz = centers[i % 3]
+        rows.append((
+            d0 + timedelta(days=i), f"g{i % 3}",
+            round(cx + random.gauss(0, 1.0), 4),
+            round(cy + random.gauss(0, 1.0), 4),
+            round(cz + random.gauss(0, 1.0), 4),
+        ))
+    con.executemany("INSERT INTO pts VALUES (?,?,?,?,?)", rows)
+    con.close()
+
+
+def test_profile_level_lite_skips_expensive_models(tmp_path):
+    """lite: el bloque models trae PCA + normalidad pero NO KMeans/IsolationForest.
+
+    Demuestra (DoD bajo consumo) que el camino lite no ejecuta los modelos caros
+    en CPU ni la capa LLM ni la serie temporal: prof['models'] queda con pca y
+    normality poblados y kmeans/outliers a None, prof['llm'] y prof['series'] a
+    None, y el capítulo `modelos` se renderiza igualmente (con PCA, sin clusters).
+    """
+    import json
+
+    db = str(tmp_path / "pts.duckdb")
+    _make_db_models(db)
+    out = str(tmp_path / "out")
+    r = render_automatic_eda(db, "pts", profile_level="lite",
+                             out_dir=out, basename="lite")
+    assert r["status"] == "ok", r.get("error")
+
+    models = (r["profile"] or {}).get("models") or {}
+    assert models.get("pca") is not None, "lite debe traer PCA"
+    assert models.get("normality") is not None, "lite debe traer normalidad"
+    assert models.get("kmeans") is None, "lite NO debe ejecutar KMeans"
+    assert models.get("outliers") is None, "lite NO debe ejecutar IsolationForest"
+    assert (r["profile"] or {}).get("llm") is None, "lite NO debe llamar al LLM"
+    assert (r["profile"] or {}).get("series") is None, "lite NO debe calcular serie"
+
+    # El capítulo modelos sigue presente (lo puebla el PCA), sin clusters KMeans.
+    with open(r["manifest_path"], encoding="utf-8") as fh:
+        man = json.load(fh)
+    assert "modelos" in (man.get("chapters") or {})
+
+
+def test_profile_level_standard_runs_full_models(tmp_path):
+    """standard (default): modelos completos (KMeans + IsolationForest) y serie."""
+    db = str(tmp_path / "pts.duckdb")
+    _make_db_models(db)
+    out = str(tmp_path / "out")
+    r = render_automatic_eda(db, "pts", profile_level="standard",
+                             out_dir=out, basename="std")
+    assert r["status"] == "ok", r.get("error")
+    models = (r["profile"] or {}).get("models") or {}
+    assert models.get("pca") is not None
+    assert models.get("kmeans") is not None, "standard debe ejecutar KMeans"
+    assert models.get("outliers") is not None, "standard debe ejecutar IsolationForest"
+    assert (r["profile"] or {}).get("series") is not None, "standard calcula serie"
+
+
+def _patch_pipeline_internals(monkeypatch, captured):
+    """Stub de las dependencias del pipeline para tests de resolución de flags.
+
+    Sustituye profile_table / build_eda_render_ctx / renderers por stubs rápidos
+    sin red ni matplotlib, capturando los kwargs con los que se invocan. Permite
+    verificar la PRECEDENCIA flag-explícito-sobre-preset sin ejecutar el EDA real.
+    """
+    import pipelines.render_automatic_eda as mod
+
+    def fake_profile_table(db_path, table, **kw):
+        captured["run_llm"] = kw.get("run_llm")
+        captured["run_models"] = kw.get("run_models")
+        captured["run_series"] = kw.get("run_series")
+        captured["sample"] = kw.get("sample")
+        return {"status": "ok", "profile": {"columns": []}}
+
+    def fake_ctx(db_path, table, prof, **kw):
+        captured["base_ctx"] = kw.get("base_ctx")
+        return {}
+
+    monkeypatch.setattr(mod, "profile_table", fake_profile_table)
+    monkeypatch.setattr(mod, "build_eda_render_ctx", fake_ctx)
+    monkeypatch.setattr(mod, "render_automatic_eda_pdf",
+                        lambda *a, **k: {"path": "x.pdf", "n_pages": 1,
+                                         "manifest_path": "m.json"})
+    monkeypatch.setattr(mod, "render_automatic_eda_pptx",
+                        lambda *a, **k: {"path": "x.pptx", "n_slides": 1})
+
+
+def test_explicit_flag_overrides_preset(monkeypatch):
+    """Precedencia: profile_level='lite' con run_llm=True explícito → LLM activo.
+
+    El flag explícito del caller gana al default del preset. Se verifica tanto en
+    el flag que llega a profile_table (run_llm=True ⇒ profile_table llamará al
+    LLM) como en el base_ctx (run_cluster_llm=True ⇒ narrativa LLM por capítulo).
+    """
+    captured = {}
+    _patch_pipeline_internals(monkeypatch, captured)
+
+    captured.clear()
+    render_automatic_eda("db", "t", profile_level="lite", run_llm=True)
+    assert captured["run_llm"] is True, "flag explícito debe primar sobre preset lite"
+    assert (captured["base_ctx"] or {}).get("run_cluster_llm") is True
+
+
+def test_full_preset_enables_llm(monkeypatch):
+    """full: el preset resuelve run_llm=True y activa la narrativa LLM en el ctx."""
+    captured = {}
+    _patch_pipeline_internals(monkeypatch, captured)
+
+    captured.clear()
+    render_automatic_eda("db", "t", profile_level="full")
+    assert captured["run_llm"] is True
+    assert (captured["base_ctx"] or {}).get("run_cluster_llm") is True
+
+
+def test_no_profile_level_defaults_to_standard(monkeypatch):
+    """Retro-compat: sin profile_level ni flags, el comportamiento es el histórico.
+
+    standard = run_models True, run_series True, run_llm False, sample 5000. Es el
+    mismo default que tenía el pipeline antes de introducir profile_level (cambio
+    aditivo: las llamadas existentes no cambian de comportamiento).
+    """
+    captured = {}
+    _patch_pipeline_internals(monkeypatch, captured)
+
+    captured.clear()
+    render_automatic_eda("db", "t")  # sin profile_level ni flags de coste
+    assert captured["run_models"] is True
+    assert captured["run_series"] is True
+    assert captured["run_llm"] is False
+    assert captured["sample"] == 5000
+
+
+def test_lite_preset_defaults(monkeypatch):
+    """lite por defecto: run_llm/run_series False y sample reducido a 2000."""
+    captured = {}
+    _patch_pipeline_internals(monkeypatch, captured)
+
+    captured.clear()
+    render_automatic_eda("db", "t", profile_level="lite")
+    assert captured["run_llm"] is False
+    assert captured["run_series"] is False
+    assert captured["sample"] == 2000

python/pyproject.toml

@@ -25,6 +25,7 @@ dependencies = [
     "polars>=1.40.1",
     "pymeshlab>=2025.7.post1",
     "pymssql>=2.3.13",
+    "pymupdf>=1.28.0",
     "pypdf>=6.10.0",
     "pyproj>=3.7.2",
     "python-docx>=1.2.0",