chore: auto-commit (799 archivos)

- .claude/CLAUDE.md
- .claude/commands/subagentes.md
- .claude/rules/INDEX.md
- .mcp.json
- bash/functions/cybersecurity/analyze_dns.md
- bash/functions/cybersecurity/audit_http_headers.md
- bash/functions/cybersecurity/audit_ssh_config.md
- bash/functions/cybersecurity/check_firewall.md
- bash/functions/cybersecurity/detect_suspicious_users.md
- bash/functions/cybersecurity/encrypt_file.md
- ...

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

python/functions/embedding/embedding_encode.md

@@ -7,7 +7,7 @@ version: "1.0.0"
 purity: impure
 signature: "def embedding_encode(model: SentenceTransformer, texts: list, mode: str = 'document') -> list"
 description: "Genera embeddings normalizados para textos. Aplica prefijos e5 automaticamente segun mode (document/query)."
-tags: [embedding, encode, e5, multilingual, python]
+tags: [embedding, encode, e5, multilingual, python, pendiente-usar]
 uses_functions: [embedding_load_model_py_infra]
 uses_types: []
 returns: []

python/functions/embedding/embedding_save_model.md

@@ -7,7 +7,7 @@ version: "1.0.0"
 purity: impure
 signature: "def embedding_save_model(model_id: str, path: str) -> str"
 description: "Descarga modelo de embeddings de HuggingFace y lo guarda en path local para carga rapida sin red."
-tags: [embedding, model, save, huggingface, e5, python]
+tags: [embedding, model, save, huggingface, e5, python, pendiente-usar]
 uses_functions: []
 uses_types: []
 returns: []

python/functions/embedding/embedding_search_sqlvec.md

@@ -7,7 +7,7 @@ version: "1.0.0"
 purity: impure
 signature: "def embedding_search_sqlvec(db_path: str, table: str, query_embedding: list, k: int = 10) -> list"
 description: "Busca los k vecinos mas cercanos en tabla sqlite-vec. Retorna rowids y distancias ordenados."
-tags: [embedding, sqlite, vector, search, retrieval, sqlite-vec, python]
+tags: [embedding, sqlite, vector, search, retrieval, sqlite-vec, python, pendiente-usar]
 uses_functions: []
 uses_types: []
 returns: []

python/functions/embedding/embedding_search_usearch.md

@@ -7,7 +7,7 @@ version: "1.0.0"
 purity: impure
 signature: "def embedding_search_usearch(path: str, query_embedding: list, k: int = 10, dim: int = 384) -> list"
 description: "Busca los k vecinos mas cercanos en indice USearch persistido. Busqueda sub-milisegundo."
-tags: [embedding, usearch, vector, search, retrieval, ann, python]
+tags: [embedding, usearch, vector, search, retrieval, ann, python, pendiente-usar]
 uses_functions: []
 uses_types: []
 returns: []

python/functions/embedding/embedding_store_sqlvec.md

@@ -7,7 +7,7 @@ version: "1.0.0"
 purity: impure
 signature: "def embedding_store_sqlvec(db_path: str, table: str, ids: list, embeddings: list, dim: int = 384) -> int"
 description: "Inserta embeddings en tabla sqlite-vec. Crea la tabla virtual si no existe. Insercion en batches."
-tags: [embedding, sqlite, vector, store, sqlite-vec, python]
+tags: [embedding, sqlite, vector, store, sqlite-vec, python, pendiente-usar]
 uses_functions: []
 uses_types: []
 returns: []

python/functions/embedding/embedding_store_usearch.md

@@ -7,7 +7,7 @@ version: "1.0.0"
 purity: impure
 signature: "def embedding_store_usearch(path: str, ids: list, embeddings: list, dim: int = 384) -> int"
 description: "Crea indice USearch con embeddings y lo persiste a archivo. Busqueda sub-milisegundo."
-tags: [embedding, usearch, vector, store, ann, python]
+tags: [embedding, usearch, vector, store, ann, python, pendiente-usar]
 uses_functions: []
 uses_types: []
 returns: []