notebook-kb

name	notebook-kb
description	Use the notebook knowledge base (the local SQLite RAG /agy:notebook builds from a folder of documents) to do precise, grounded, cited work — total amounts by category, find every doc that mentions a person/organization, build a timeline, export a table — instead of re-reading prose and burning Claude's context. Trigger on "sum the amounts", "which docs mention X", "build a timeline", "who/what/when across these documents", or any aggregate/lookup over an analyzed corpus.
user-invocable	true

notebook-kb — work against the local document RAG

/agy:notebook <folder> | <objective> analyzes a folder of documents and compiles a queryable SQLite database docs/agy/notebook/<slug>/notebook.db: documents, chunks (+FTS5 / optional vectors), entities, events, relations, citations. Every fact row carries a quote and a source document. This skill is how you USE that DB to do real work — deterministically, with citations, and without pulling the documents back into Claude's context.

Decision gate — when to use the DB

• Use the DB (/agy:notebook-query) for structured / aggregate / grounding work: totals of amounts by category, "which documents mention <person/org/term>", timelines, entity rosters, exporting a table, verifying a figure against its source. SQL is exact and auditable; prose is not.
• Use /agy:notebook-ask for an open-ended prose answer grounded in the summaries.
• Build/refresh first if needed: if notebook.db is missing → run /agy:notebook <folder> | <objective>. If it's older than the newest *.facts.json → rebuild (Phase 1.5): python "<plugin>/scripts/notebook_db.py" "<OUTDIR>" "<objective>" (~1s, pure Python).

How to query (there is NO sqlite3 CLI — always Python, read-only)

python - "<OUTDIR>/notebook.db" "<SQL>" <<'PY'
import sqlite3, sys, json
con = sqlite3.connect("file:%s?mode=ro" % sys.argv[1], uri=True); con.row_factory = sqlite3.Row
try: print(json.dumps([dict(r) for r in con.execute(sys.argv[2])], ensure_ascii=False, indent=2, default=str))
except Exception as e: print("SQL_ERROR: %s" % e)
PY

Prefer the v_* views (they dedup by ent_key and keep citations). The schema + a recetas cookbook live in the /agy:notebook-query command file — reuse those queries. Entity taxonomy: persona | organizacion | monto | fecha | referencia.

Citation contract (non-negotiable for trustworthy answers)

• Every claim cites its source: doc_ref (or basename) of the document the row came from.
• A SUM lists its contributing rows so the total is auditable line by line. Monetary math is in integer monto_cents; divide by 100 only to display (no float drift).
• 0 rows → say "does not appear in the corpus", and surface coverage gaps: SELECT nn,tipo,basename FROM documents WHERE estado='no_procesado'. Never invent a name, amount, date or reference — if it isn't a row in the DB, it isn't a fact.

Downstream workflows (turn the DB into deliverables)

• Entity roster → SELECT * FROM v_personas / v_organizaciones / v_referencias.
• Timeline → SELECT * FROM v_timeline → a chronological briefing.
• Export a table → query amounts by category (or any view), emit a small CSV/JSON, and hand it to whatever downstream tool or report consumes it — instead of transcribing figures from hundreds of pages by hand. Cross-check a computed total against the DB's v_montos total before presenting.
• Contradiction check → /agy:notebook-audit <folder> flags the same category with conflicting amounts, the same person/org under two names, the same reference with different values, and gaps.

Semantic search (opt-in)

By default retrieval is FTS5 keyword (always on, zero deps). For fuzzy/conceptual questions add a vector layer: build with /agy:notebook <folder> | <objective> --semantic (needs pip install sqlite-vec; real embeddings need a GEMINI_API_KEY, else a keyword-ish lexical fallback). Then /agy:notebook-query fuses keyword + vector ranking with RRF. Without it, keyword + structured SQL already answer most aggregate/lookup work.

Long sweeps & cross-session

• Long document sets — run /agy:notebook <folder> | <objective> --background and check progress with /agy:notebook-status <folder> (% done, ETA, pending docs). The sweep persists state every wave, so it's resumable: re-run /agy:notebook and cached docs are skipped. No daemon.
• Cross-folder in Neon (opt-in) — to query MANY notebooks together, export one KB to Postgres SQL with scripts/notebook_neon.py <OUTDIR> <notebook_name> (writes nbkb_export.sql, an isolated nbkb schema), then run it via the Neon MCP (mcp__neon__run_sql). Only worth it for cross-folder aggregation; the local notebook.db already answers single-folder questions.

Reliability notes

• The DB is disposable (gitignored) and always rebuildable from the .facts.json sidecars; the .md summaries remain the human source of truth.
• The loader is tolerant: malformed/missing sidecars fall back to the .md frontmatter and are logged to _facts_errors.log — the document is still queryable by tipo/fecha/doc_ref.