diff --git a/python/functions/datascience/automatic_eda/chapters/modelos.py b/python/functions/datascience/automatic_eda/chapters/modelos.py
new file mode 100644
index 00000000..ffc43346
--- /dev/null
+++ b/python/functions/datascience/automatic_eda/chapters/modelos.py
@@ -0,0 +1,498 @@
+"""Models chapter (MODELOS) — cheap unsupervised models, rendered as markdown.
+
+Builds the *Modelos* chapter of an AutomaticEDA document from the ``models``
+block of a TableProfile (``run_eda_models`` output: ``{pca, kmeans, outliers,
+normality}``). It renders, as structured markdown/tables/figures that the core
+paginator never cuts:
+
+1. **Normalization note** — every multivariate model below standardizes the
+   columns with z-score first; the chapter explains why (different scales would
+   otherwise dominate distance/variance).
+2. **PCA** — a scree plot (explained + cumulative variance, single Y axis) plus
+   variance and top-loadings tables.
+3. **KMeans segments** — a PCA scatter **coloured by cluster** (its own
+   page/slide), the cluster-size table, and a per-cluster LLM micro-analysis
+   with a title for each segment.
+4. **Isolation Forest outliers** — a short explanation of how anomalous rows are
+   isolated multivariately and how the threshold is chosen, plus the counts.
+5. **Normality** — per-column Jarque-Bera / D'Agostino / Shapiro verdicts.
+
+The raw numeric data needed to colour the cluster scatter is **not** in the
+TableProfile, so — exactly like ``overview`` reads ``head_rows`` from ``ctx`` —
+this chapter looks for the cluster projection / raw numeric columns in ``ctx``
+(or in ``profile``) and degrades honestly when they are absent: it falls back to
+the uncoloured ``pca.projection`` with a note, or omits the scatter entirely.
+
+ctx keys this chapter consumes (all optional):
+    cluster_projection : dict — a pre-computed ``project_clusters_2d`` result
+        (``points``/``labels``/``centers_2d``/``cluster_profiles``/...). Used
+        directly when present (forward-compatible with the calculation phase).
+    raw_numeric : dict — ``{col: [values]}`` raw numeric columns; when present
+        and ``cluster_projection`` is not, the chapter calls
+        ``project_clusters_2d`` live to build points + aligned labels.
+    cluster_titles : list — pre-computed ``[{cluster, title, description}]``
+        (a ``describe_clusters_llm`` ``clusters`` list). Used for the per-cluster
+        micro-analysis without an LLM call (offline/tests).
+    run_cluster_llm : bool — when True and ``cluster_titles`` is absent, call
+        ``describe_clusters_llm`` live on the cluster profiles.
+    cluster_llm_model : str — model id for the live LLM call.
+
+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 = "modelos"
+CHAPTER_TITLE = "Modelos"
+
+# Tableau-10 palette (matplotlib's default cycle) — used both for the matplotlib
+# scatter and to keep the legend/colours stable per cluster index.
+_CLUSTER_COLORS = [
+    "#4e79a7", "#f28e2b", "#e15759", "#76b7b2", "#59a14f",
+    "#edc948", "#b07aa1", "#ff9da7", "#9c755f", "#bab0ac",
+]
+
+
+# --------------------------------------------------------------------------- #
+# Formatting helpers (mirror the overview chapter's defensive style).
+# --------------------------------------------------------------------------- #
+def _fmt_num(value, decimals: int = 3) -> str:
+    if value is None:
+        return "—"
+    if isinstance(value, bool):
+        return "sí" if value else "no"
+    if isinstance(value, int):
+        return f"{value:,}".replace(",", ".")
+    if isinstance(value, float):
+        if value != value:  # NaN
+            return "NaN"
+        if value in (float("inf"), float("-inf")):
+            return str(value)
+        text = f"{value:.{decimals}f}".rstrip("0").rstrip(".")
+        return text if text else "0"
+    return model._safe_str(value)
+
+
+def _fmt_pct_ratio(value, decimals: int = 1) -> str:
+    """Format a 0..1 ratio as a percentage."""
+    if value is None:
+        return "—"
+    try:
+        return f"{float(value) * 100:.{decimals}f}%"
+    except (TypeError, ValueError):
+        return model._safe_str(value)
+
+
+def _fmt_pct_already(value, decimals: int = 2) -> str:
+    """Format a value that is *already* a 0..100 percentage."""
+    if value is None:
+        return "—"
+    try:
+        return f"{float(value):.{decimals}f}%"
+    except (TypeError, ValueError):
+        return model._safe_str(value)
+
+
+def _is_dict(v) -> bool:
+    return isinstance(v, dict)
+
+
+# --------------------------------------------------------------------------- #
+# Cluster projection: prefer a pre-computed result, else compute it live, else
+# fall back to the uncoloured PCA projection.
+# --------------------------------------------------------------------------- #
+def _resolve_cluster_projection(profile: dict, ctx: dict):
+    """Return (projection_dict_or_None, source_label).
+
+    Order: ctx/profile['cluster_projection'] (pre-computed) → live
+    project_clusters_2d on ctx/profile['raw_numeric'] → None.
+    """
+    pre = ctx.get("cluster_projection") or profile.get("cluster_projection")
+    models = profile.get("models") if _is_dict(profile.get("models")) else {}
+    if not pre and _is_dict(models):
+        pre = models.get("cluster_projection")
+    if _is_dict(pre) and pre.get("points"):
+        return pre, "precomputed"
+
+    raw = ctx.get("raw_numeric") or profile.get("raw_numeric")
+    if _is_dict(raw) and raw:
+        try:
+            # Import the submodule's function explicitly (avoid the package
+            # attribute shadowing the function with the same-named module).
+            from datascience.project_clusters_2d import project_clusters_2d
+            proj = project_clusters_2d(raw)
+            if _is_dict(proj) and proj.get("points"):
+                return proj, "live"
+        except Exception:  # noqa: BLE001 — never break the chapter.
+            return None, "none"
+    return None, "none"
+
+
+def _cluster_titles(profile: dict, ctx: dict, projection: dict):
+    """Return a list of {cluster, title, description} for the segments.
+
+    Order: ctx['cluster_titles'] (pre-computed) → live describe_clusters_llm when
+    ctx['run_cluster_llm'] and we have cluster_profiles → derived titles from the
+    distinctive features → None.
+    """
+    pre = ctx.get("cluster_titles")
+    if isinstance(pre, list) and pre:
+        return [c for c in pre if _is_dict(c)]
+
+    profiles = (projection or {}).get("cluster_profiles") or []
+    feats = (projection or {}).get("feature_names") or []
+    if ctx.get("run_cluster_llm") and profiles:
+        try:
+            from datascience.describe_clusters_llm import describe_clusters_llm
+            out = describe_clusters_llm(
+                profiles, feats,
+                model=ctx.get("cluster_llm_model", "claude-haiku-4-5-20251001"))
+            clusters = (out or {}).get("clusters")
+            if isinstance(clusters, list) and clusters:
+                return [c for c in clusters if _is_dict(c)]
+        except Exception:  # noqa: BLE001
+            pass
+
+    # Derived fallback: name each cluster by its distinctive features.
+    if profiles:
+        derived = []
+        for p in profiles:
+            if not _is_dict(p):
+                continue
+            cid = p.get("cluster", len(derived))
+            dist = p.get("distinctive") or []
+            label = ", ".join(model._safe_str(d) for d in dist[:2]) if dist else ""
+            title = f"Segmento {cid}" + (f" — {label}" if label else "")
+            derived.append({"cluster": cid, "title": title, "description": ""})
+        if derived:
+            return derived
+    return None
+
+
+# --------------------------------------------------------------------------- #
+# Figure builders (lazy: matplotlib only imported when the renderer draws them).
+# --------------------------------------------------------------------------- #
+def _make_scree(pca: dict):
+    """Return a zero-arg callable drawing the PCA scree plot, or None."""
+    evr = pca.get("explained_variance_ratio") or []
+    cum = pca.get("cumulative") or []
+    if not evr:
+        return None
+
+    def _draw():
+        import matplotlib
+        matplotlib.use("Agg")
+        import matplotlib.pyplot as plt
+
+        comps = list(range(1, len(evr) + 1))
+        fig, ax = plt.subplots(figsize=(7.0, 4.2))
+        ax.bar(comps, evr, color="#4e79a7", alpha=0.85,
+               label="Varianza explicada")
+        if cum:
+            ax.plot(comps[:len(cum)], cum, color="#e15759", marker="o",
+                    linewidth=1.8, label="Acumulada")
+        ax.set_xlabel("Componente principal")
+        ax.set_ylabel("Proporción de varianza")
+        ax.set_xticks(comps)
+        ax.set_ylim(0, 1.0)
+        ax.grid(axis="y", color="#dddddd", linewidth=0.6)
+        ax.legend(loc="best", fontsize=8, frameon=False)
+        ax.set_title("Varianza explicada por componente (PCA)", fontsize=10)
+        fig.tight_layout()
+        return fig
+
+    return _draw
+
+
+def _make_cluster_scatter(projection: dict):
+    """Return a zero-arg callable drawing the cluster scatter, or None."""
+    points = projection.get("points") or []
+    labels = projection.get("labels") or []
+    if not points or len(points) != len(labels):
+        return None
+    centers = projection.get("centers_2d") or []
+    explained = projection.get("explained_2d") or []
+
+    def _draw():
+        import matplotlib
+        matplotlib.use("Agg")
+        import matplotlib.pyplot as plt
+
+        fig, ax = plt.subplots(figsize=(7.0, 5.2))
+        uniq = sorted(set(int(l) for l in labels))
+        for cl in uniq:
+            xs = [p[0] for p, l in zip(points, labels) if int(l) == cl]
+            ys = [p[1] for p, l in zip(points, labels) if int(l) == cl]
+            color = _CLUSTER_COLORS[cl % len(_CLUSTER_COLORS)]
+            ax.scatter(xs, ys, s=14, c=color, alpha=0.7, linewidths=0,
+                       label=f"Cluster {cl} (n={len(xs)})")
+        for cl, c in enumerate(centers):
+            color = _CLUSTER_COLORS[cl % len(_CLUSTER_COLORS)]
+            ax.scatter([c[0]], [c[1]], s=180, c=color, marker="X",
+                       edgecolors="black", linewidths=1.2, zorder=5)
+        xlab, ylab = "PC1", "PC2"
+        if len(explained) >= 2:
+            xlab = f"PC1 ({_fmt_pct_ratio(explained[0])} var.)"
+            ylab = f"PC2 ({_fmt_pct_ratio(explained[1])} var.)"
+        ax.set_xlabel(xlab)
+        ax.set_ylabel(ylab)
+        ax.set_title("Segmentos KMeans proyectados sobre el plano PCA",
+                     fontsize=10)
+        ax.grid(color="#eeeeee", linewidth=0.5)
+        ax.legend(loc="best", fontsize=8, frameon=True, framealpha=0.9)
+        fig.tight_layout()
+        return fig
+
+    return _draw
+
+
+# --------------------------------------------------------------------------- #
+# Section builders. Each returns a list of blocks (possibly empty).
+# --------------------------------------------------------------------------- #
+def _normalization_intro() -> list:
+    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, "
+        "dividido por la desviación típica). Sin esta normalización, una "
+        "variable con escala grande (p.ej. ingresos en euros) dominaría las "
+        "distancias y la varianza frente a otra de escala pequeña (p.ej. un "
+        "ratio entre 0 y 1), sesgando tanto el PCA como el KMeans. Tras la "
+        "estandarización todas las variables pesan por igual."
+    )
+    return [model.Heading(text="Modelos no supervisados", level=1),
+            model.Markdown(text=text)]
+
+
+def _pca_section(pca: dict) -> list:
+    if not _is_dict(pca) or not pca.get("explained_variance_ratio"):
+        return []
+    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"ortogonales ordenados por la varianza que capturan "
+        f"({_fmt_num(n_used)} filas usadas tras eliminar nulos). El gráfico de "
+        "sedimentación (scree) muestra cuánta varianza aporta cada componente y "
+        "su acumulado: un codo marca cuántos componentes bastan."
+    )
+    blocks.append(model.Markdown(text=intro))
+
+    scree = _make_scree(pca)
+    if scree is not None:
+        blocks.append(model.Figure(
+            make=scree, caption="Varianza explicada y acumulada por componente."))
+
+    evr = pca.get("explained_variance_ratio") or []
+    cum = pca.get("cumulative") or []
+    rows = []
+    for i, v in enumerate(evr):
+        acc = cum[i] if i < len(cum) else None
+        rows.append([f"PC{i + 1}", _fmt_pct_ratio(v), _fmt_pct_ratio(acc)])
+    if rows:
+        blocks.append(model.DataTable(
+            header=["Componente", "Varianza", "Acumulada"], rows=rows,
+            title="Varianza por componente"))
+
+    # Top loadings: keep the strongest features per component (capped).
+    loadings = pca.get("top_loadings") or []
+    if loadings:
+        per_comp: dict = {}
+        for ld in loadings:
+            if not _is_dict(ld):
+                continue
+            comp = ld.get("component")
+            per_comp.setdefault(comp, [])
+            if len(per_comp[comp]) < 4:
+                per_comp[comp].append(ld)
+        rows = []
+        for comp in sorted(per_comp.keys(), key=lambda x: (x is None, x)):
+            for ld in per_comp[comp]:
+                rows.append([f"PC{int(comp) + 1}" if comp is not None else "—",
+                             model._safe_str(ld.get("feature")),
+                             _fmt_num(ld.get("loading"))])
+        if rows:
+            blocks.append(model.DataTable(
+                header=["Componente", "Variable", "Carga"], rows=rows,
+                title="Cargas principales (top por componente)",
+                note="Cargas con mayor valor absoluto: qué variables definen "
+                     "cada eje."))
+    return blocks
+
+
+def _kmeans_section(kmeans: dict, projection: dict, titles) -> 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 []
+
+    blocks = [model.Heading(text="Segmentación (KMeans)", level=2)]
+
+    best_k = (projection or {}).get("best_k") or (kmeans or {}).get("best_k")
+    sil = (projection or {}).get("silhouette")
+    if sil is None:
+        sil = (kmeans or {}).get("silhouette")
+    intro = (
+        f"KMeans agrupa las filas en **{_fmt_num(best_k)} segmentos** elegidos "
+        "automáticamente maximizando el coeficiente de *silhouette* "
+        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."
+    )
+    blocks.append(model.Markdown(text=intro))
+
+    if has_proj:
+        scatter = _make_cluster_scatter(projection)
+        if scatter is not None:
+            blocks.append(model.Figure(
+                make=scatter,
+                caption="Cada punto es una fila coloreada por su segmento "
+                        "KMeans; las «X» son los centroides."))
+        else:
+            blocks.append(model.Note(
+                "Proyección de clusters no dibujable (puntos y etiquetas "
+                "desalineados)."))
+    else:
+        # We have kmeans stats but no aligned points+labels to colour by.
+        blocks.append(model.Note(
+            "Scatter coloreado por segmento no disponible: el perfil no incluye "
+            "la proyección con etiquetas alineadas (pásala en "
+            "ctx['cluster_projection'] o las columnas crudas en "
+            "ctx['raw_numeric'] para colorear el plano PCA)."))
+
+    # Cluster sizes table.
+    sizes = (projection or {}).get("cluster_sizes") or (kmeans or {}).get("cluster_sizes") or []
+    total = sum(s for s in sizes if isinstance(s, (int, float))) or 0
+    if sizes:
+        rows = []
+        for i, s in enumerate(sizes):
+            pct = (s / total) if total else None
+            rows.append([f"Cluster {i}", _fmt_num(s), _fmt_pct_ratio(pct)])
+        blocks.append(model.DataTable(
+            header=["Segmento", "Tamaño", "% del total"], rows=rows,
+            title="Tamaño de cada segmento"))
+
+    # Per-cluster LLM micro-analysis (each entry kept indivisible as one block).
+    if titles:
+        blocks.append(model.Heading(text="Interpretación de los segmentos",
+                                    level=3))
+        for t in titles:
+            if not _is_dict(t):
+                continue
+            cid = t.get("cluster")
+            title = model._safe_str(t.get("title")) or f"Cluster {cid}"
+            desc = model._safe_str(t.get("description"))
+            line = f"**Cluster {cid} — {title}.**"
+            if desc:
+                line += " " + desc
+            blocks.append(model.Markdown(text=line))
+    return blocks
+
+
+def _outliers_section(outliers: dict) -> 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 []
+    blocks = [model.Heading(text="Detección de anomalías (Isolation Forest)",
+                            level=2)]
+    explain = (
+        "**Isolation Forest** 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 "
+        "columnas a la vez**, no una sola) se aíslan con muy pocos cortes y "
+        "obtienen un score bajo. El **umbral** de decisión separa las filas "
+        "normales de las anómalas según la contaminación esperada del modelo: "
+        "una fila es outlier cuando su score queda por debajo de ese umbral."
+    )
+    blocks.append(model.Markdown(text=explain))
+    blocks.append(model.KVTable(rows=[
+        ("Filas analizadas", _fmt_num(outliers.get("n_rows_used"))),
+        ("Outliers detectados", _fmt_num(outliers.get("n_outliers"))),
+        ("% outliers", _fmt_pct_already(outliers.get("outlier_pct"))),
+        ("Umbral de decisión", _fmt_num(outliers.get("threshold"), 4)),
+    ], title="Anomalías multivariantes"))
+    return blocks
+
+
+def _normality_section(normality: dict) -> list:
+    if not _is_dict(normality) or not normality:
+        return []
+    header = ["Columna", "Jarque-Bera (p)", "D'Agostino (p)", "Shapiro (p)",
+              "¿Normal?"]
+    rows = []
+    for col, res in normality.items():
+        if not _is_dict(res):
+            continue
+        jb = res.get("jarque_bera") if _is_dict(res.get("jarque_bera")) else {}
+        da = res.get("dagostino") if _is_dict(res.get("dagostino")) else {}
+        sh = res.get("shapiro") if _is_dict(res.get("shapiro")) else {}
+        is_norm = res.get("is_normal")
+        if res.get("note") and is_norm is None and not jb:
+            rows.append([model._safe_str(col), "—", "—", "—",
+                         model._safe_str(res.get("note"))])
+            continue
+        rows.append([
+            model._safe_str(col),
+            _fmt_num(jb.get("p"), 4) if jb else "—",
+            _fmt_num(da.get("p"), 4) if da else "—",
+            _fmt_num(sh.get("p"), 4) if sh else "—",
+            "sí" if is_norm else ("no" if is_norm is not None else "—"),
+        ])
+    if not rows:
+        return []
+    return [
+        model.Heading(text="Normalidad de las variables", level=2),
+        model.Markdown(text=(
+            "Tests de hipótesis de normalidad por columna (hipótesis nula: la "
+            "muestra proviene de una distribución normal). Se marca **normal** "
+            "cuando el p-valor supera 0,05 (no se rechaza la nula). Pocas "
+            "variables reales son estrictamente normales; esto orienta qué "
+            "transformaciones o tests robustos aplicar después.")),
+        model.DataTable(header=header, rows=rows,
+                        title="Pruebas de normalidad"),
+    ]
+
+
+# --------------------------------------------------------------------------- #
+# Entry point.
+# --------------------------------------------------------------------------- #
+def build_modelos(profile: dict, ctx: dict):
+    """Build the MODELOS Chapter, or None if there are no models to show."""
+    profile = profile or {}
+    ctx = ctx or {}
+    if not isinstance(profile, dict):
+        return None
+    models = profile.get("models")
+    if not _is_dict(models):
+        return None
+
+    pca = models.get("pca") if _is_dict(models.get("pca")) else None
+    kmeans = models.get("kmeans") if _is_dict(models.get("kmeans")) else None
+    outliers = models.get("outliers") if _is_dict(models.get("outliers")) else None
+    normality = models.get("normality") if _is_dict(models.get("normality")) else None
+
+    projection, _src = _resolve_cluster_projection(profile, ctx)
+    titles = _cluster_titles(profile, ctx, projection) if (
+        (kmeans and kmeans.get("best_k")) or (projection and projection.get("points"))
+    ) else None
+
+    sections = []
+    sections += _pca_section(pca) if pca else []
+    sections += _kmeans_section(kmeans, projection, titles)
+    sections += _outliers_section(outliers) 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
+    return model.Chapter(id=CHAPTER_ID, title=CHAPTER_TITLE,
+                         version=CHAPTER_VERSION, blocks=blocks)
diff --git a/python/functions/datascience/automatic_eda/chapters/modelos_test.py b/python/functions/datascience/automatic_eda/chapters/modelos_test.py
new file mode 100644
index 00000000..9d2597a5
--- /dev/null
+++ b/python/functions/datascience/automatic_eda/chapters/modelos_test.py
@@ -0,0 +1,259 @@
+"""Tests for the MODELOS chapter — DoD: golden + edges + anti-cut.
+
+Self-contained: builds a synthetic TableProfile with a ``models`` block (no
+DuckDB, no sklearn, no LLM, no network). The cluster scatter is fed a synthetic
+pre-computed ``cluster_projection`` via ``ctx`` and the per-cluster titles via
+``ctx['cluster_titles']`` so the suite is fast and deterministic. The live paths
+(``project_clusters_2d`` / ``describe_clusters_llm``) are exercised against the
+real wine dataset in the work report, not here.
+
+Verifies: the chapter renders to PDF *and* PPTX showing the user-required pieces
+(markdown text, PCA scree, cluster scatter, per-cluster LLM micro-analysis,
+outlier + normalization explanations); that an inapplicable profile yields None
+without raising; and that a long normality table is split without losing any
+column (anti-cut).
+"""
+
+import os
+import re
+import tempfile
+
+from pypdf import PdfReader
+from pptx import Presentation
+
+from datascience.automatic_eda.chapters.modelos import build_modelos
+from datascience.automatic_eda.model import Figure, DataTable, Markdown
+from datascience.render_automatic_eda_pdf import render_automatic_eda_pdf
+from datascience.render_automatic_eda_pptx import render_automatic_eda_pptx
+
+
+# --------------------------------------------------------------------------- #
+# Synthetic fixtures.
+# --------------------------------------------------------------------------- #
+def _models_block(n_norm_cols: int = 4) -> dict:
+    feats = ["fixed_acidity", "alcohol", "ph", "sulphates"]
+    normality = {}
+    for i in range(n_norm_cols):
+        normality[f"col_{i}"] = {
+            "n": 500,
+            "jarque_bera": {"stat": 12.3, "p": 0.002 + i * 0.0001, "normal": False},
+            "dagostino": {"stat": 9.1, "p": 0.01, "normal": False},
+            "shapiro": {"stat": 0.98, "p": 0.04, "normal": False},
+            "is_normal": False,
+        }
+    return {
+        "n_numeric_cols": 4,
+        "pca": {
+            "n_components": 2, "n_rows_used": 1599, "n_features": 4,
+            "explained_variance_ratio": [0.41, 0.22],
+            "cumulative": [0.41, 0.63],
+            "top_loadings": [
+                {"component": 0, "feature": "alcohol", "loading": 0.62},
+                {"component": 0, "feature": "fixed_acidity", "loading": -0.48},
+                {"component": 1, "feature": "ph", "loading": 0.71},
+                {"component": 1, "feature": "sulphates", "loading": 0.33},
+            ],
+            "projection": [[0.1, 0.2], [0.3, -0.1]],
+        },
+        "kmeans": {
+            "best_k": 3, "silhouette": 0.27,
+            "scores_by_k": [{"k": 2, "silhouette": 0.21}, {"k": 3, "silhouette": 0.27}],
+            "cluster_sizes": [700, 500, 399],
+            "centers": [[0.1, 0.2, 0.3, 0.4]],
+            "n_rows_used": 1599, "n_features": 4,
+        },
+        "outliers": {
+            "n_outliers": 80, "outlier_pct": 5.0, "threshold": -0.0123,
+            "n_rows_used": 1599,
+        },
+        "normality": normality,
+        "note": "",
+        "_feats": feats,
+    }
+
+
+def _cluster_projection() -> dict:
+    # 30 points across 3 clusters, aligned points<->labels.
+    points, labels = [], []
+    centers = [(-2.0, -2.0), (2.0, 0.0), (0.0, 2.5)]
+    for cl, (cx, cy) in enumerate(centers):
+        for j in range(10):
+            points.append([cx + (j - 5) * 0.05, cy + (j - 5) * 0.05])
+            labels.append(cl)
+    return {
+        "points": points, "labels": labels,
+        "centers_2d": [list(c) for c in centers],
+        "best_k": 3, "silhouette": 0.27,
+        "explained_2d": [0.41, 0.22],
+        "cluster_sizes": [10, 10, 10],
+        "cluster_profiles": [
+            {"cluster": 0, "size": 10, "pct": 0.33,
+             "centroid_original": {"alcohol": 9.5, "ph": 3.5},
+             "distinctive": ["alcohol", "ph"], "centroid_z": {"alcohol": -1.2}},
+            {"cluster": 1, "size": 10, "pct": 0.33,
+             "centroid_original": {"alcohol": 12.0, "ph": 3.1},
+             "distinctive": ["alcohol"], "centroid_z": {"alcohol": 1.4}},
+            {"cluster": 2, "size": 10, "pct": 0.33,
+             "centroid_original": {"alcohol": 10.5, "ph": 3.8},
+             "distinctive": ["ph"], "centroid_z": {"ph": 1.6}},
+        ],
+        "feature_names": ["alcohol", "ph", "fixed_acidity", "sulphates"],
+        "n_used": 1599, "note": "",
+    }
+
+
+def _ctx_full() -> dict:
+    return {
+        "cluster_projection": _cluster_projection(),
+        "cluster_titles": [
+            {"cluster": 0, "title": "Vinos suaves de baja graduación",
+             "description": "Alcohol bajo y pH alto; perfil ligero."},
+            {"cluster": 1, "title": "Vinos potentes",
+             "description": "Alta graduación alcohólica."},
+            {"cluster": 2, "title": "Vinos de pH elevado",
+             "description": "Acidez baja relativa al resto."},
+        ],
+    }
+
+
+def _profile() -> dict:
+    return {"table": "wine", "n_rows": 1599, "n_cols": 12,
+            "models": _models_block()}
+
+
+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)
+    out = []
+    for slide in prs.slides:
+        for shape in slide.shapes:
+            if shape.has_text_frame:
+                out.append(shape.text_frame.text)
+    return re.sub(r"\s+", " ", " ".join(out))
+
+
+# --------------------------------------------------------------------------- #
+# Golden.
+# --------------------------------------------------------------------------- #
+def test_golden_build_modelos_bloques_requeridos():
+    ch = build_modelos(_profile(), _ctx_full())
+    assert ch is not None
+    assert ch.id == "modelos" and ch.version
+    # Both figures present: scree plot + cluster scatter.
+    n_figures = sum(1 for b in ch.blocks if isinstance(b, Figure))
+    assert n_figures >= 2
+    # Tables present (variance, loadings, sizes, normality).
+    assert sum(1 for b in ch.blocks if isinstance(b, DataTable)) >= 3
+    # Markdown carries the required explanations.
+    md = " ".join(b.text for b in ch.blocks if isinstance(b, Markdown))
+    assert "z-score" in md                       # normalization explained
+    assert "Isolation Forest" in md              # outlier generation explained
+    assert "silhouette" in md                    # kmeans
+    # Per-cluster micro-analysis titles present.
+    assert "Vinos potentes" in md
+    assert "Cluster 1" in md
+
+
+def test_golden_render_pdf_muestra_lo_exigido():
+    with tempfile.TemporaryDirectory() as d:
+        out = os.path.join(d, "modelos.pdf")
+        res = render_automatic_eda_pdf(
+            _profile(), out, {"title": "EDA — wine", "ctx": _ctx_full()})
+        assert res["path"] == out and os.path.exists(out)
+        ids = [c["id"] for c in res["chapters"]]
+        assert "modelos" in ids
+        txt = _pdf_text(out)
+        for needle in ("Modelos no supervisados", "z-score", "PCA",
+                       "Segmentación", "Isolation Forest", "Normalidad",
+                       "Vinos potentes"):
+            assert needle in txt, f"falta en PDF: {needle}"
+
+
+def test_golden_render_pptx_muestra_lo_exigido():
+    with tempfile.TemporaryDirectory() as d:
+        out = os.path.join(d, "modelos.pptx")
+        res = render_automatic_eda_pptx(
+            _profile(), out, {"title": "EDA — wine", "ctx": _ctx_full()})
+        assert res["path"] == out and os.path.exists(out)
+        assert res["n_slides"] >= 1
+        txt = _pptx_text(out)
+        for needle in ("Modelos no supervisados", "z-score", "Isolation Forest",
+                       "Vinos potentes"):
+            assert needle in txt, f"falta en PPTX: {needle}"
+
+
+# --------------------------------------------------------------------------- #
+# Edges.
+# --------------------------------------------------------------------------- #
+def test_edge_profile_none_o_vacio_devuelve_none():
+    assert build_modelos(None, {}) is None
+    assert build_modelos({}, {}) is None
+    assert build_modelos({"n_rows": 5}, None) is None  # no 'models' key
+
+
+def test_edge_models_insuficiente_devuelve_none():
+    prof = {"table": "tiny", "models": {
+        "n_numeric_cols": 1,
+        "pca": {"n_components": 0, "explained_variance_ratio": [],
+                "note": "datos insuficientes"},
+        "kmeans": {"best_k": 0, "note": "datos insuficientes"},
+        "outliers": {"n_outliers": 0, "note": "datos insuficientes"},
+        "normality": None,
+        "note": "insuficientes columnas numericas para modelos multivariantes",
+    }}
+    assert build_modelos(prof, {}) is None
+
+
+def test_edge_solo_normalidad_si_genera_capitulo():
+    # A single numeric column: only normality applies. Chapter must still build.
+    prof = {"table": "one", "models": {
+        "n_numeric_cols": 1, "pca": None, "kmeans": None, "outliers": None,
+        "normality": {"x": {"n": 500, "jarque_bera": {"stat": 1.0, "p": 0.2,
+                      "normal": True}, "dagostino": {"stat": 1.0, "p": 0.3,
+                      "normal": True}, "shapiro": {"stat": 0.99, "p": 0.4,
+                      "normal": True}, "is_normal": True}},
+    }}
+    ch = build_modelos(prof, {})
+    assert ch is not None
+    md = " ".join(b.text for b in ch.blocks if isinstance(b, Markdown))
+    assert "z-score" in md  # normalization intro still present
+
+
+def test_edge_kmeans_sin_proyeccion_degrada_sin_romper():
+    # kmeans stats present but no cluster_projection / raw_numeric to colour by.
+    prof = _profile()
+    ch = build_modelos(prof, {})  # no ctx projection
+    assert ch is not None
+    # No scatter figure for clusters, but a Note explaining the degradation.
+    notes = [b.text for b in ch.blocks if b.kind == "note"]
+    assert any("ctx['raw_numeric']" in n or "cluster_projection" in n
+               for n in notes)
+    # PDF still renders fine.
+    with tempfile.TemporaryDirectory() as d:
+        out = os.path.join(d, "deg.pdf")
+        res = render_automatic_eda_pdf(prof, out, {"write_manifest": False})
+        assert res["path"] == out and os.path.exists(out)
+
+
+# --------------------------------------------------------------------------- #
+# Anti-cut.
+# --------------------------------------------------------------------------- #
+def test_anticortes_tabla_normalidad_larga_no_corta():
+    # 40 numeric columns → the normality DataTable must split across pages,
+    # repeating the header, without losing any column name.
+    prof = {"table": "wide", "models": _models_block(n_norm_cols=40)}
+    with tempfile.TemporaryDirectory() as d:
+        out = os.path.join(d, "wide.pdf")
+        render_automatic_eda_pdf(prof, out, {"write_manifest": False,
+                                             "ctx": _ctx_full()})
+        reader = PdfReader(out)
+        n_pages = len(reader.pages)
+        assert n_pages > 1
+        txt = "".join((pg.extract_text() or "") for pg in reader.pages)
+        # Every column name survives (wrapped/split, never truncated).
+        for i in (0, 19, 39):
+            assert f"col_{i}" in txt