search

OBJECTIVE: Find specific words/phrases inside Anton's entire conversation corpus (~99k notes in 01-Conversations/**) instantly and token-free, returning ranked snippets + which chats match by title. The lexical (BM25) lane of the unified search layer; the semantic (sqlite-vec + RRF) lane is added per the decision memo.

CONTEXT:

• Engine: $IMPORTS_ROOT/search/search_catalog.db (SQLite FTS5 over title+body of every conversation .md). Rebuilt by build_catalog_fts.py (derived, rebuildable artifact; ~3 min over 99k files).
• CLI: python $IMPORTS_ROOT/search/search.py [--k N] [--chats] <query> (UTF-8 output, real Cyrillic).
• Visual UI: python $IMPORTS_ROOT/search/search_server.py -> http://127.0.0.1:8771 (stdlib, no deps; autostart task keeps it alive).
• Title index reused: ..\dialogs\dialogs.db (chat lookup by name).
• Canon: vault 02-Decisions\decision-unified-search-layer.md; memory unified-search-layer. Token law: exact/SQL before BM25, BM25 before embeddings, retrieval before LLM.

STEPS:

1. Take Anton's query words. If he wants the visual interface, point him to http://127.0.0.1:8771 (start search_server.py if down).
2. Run: python $IMPORTS_ROOT/search/search.py --k 15 --chats <query> (add --chats when he's hunting for WHICH chat, not just content).
3. Read the ranked hits (lower bm25 score = more relevant) + snippets. Summarize the top matches for Anton with their file links and the chat/source they came from; offer to open or dig deeper.
4. If results look stale or a chat was recently imported, note that the catalog may need a rebuild (build_catalog_fts.py) — the weekly task handles this, or run it on demand.

CONSTRAINTS:

• Read-only over the catalog. Never write to the vault from this skill.
• Keep it token-cheap: this is deterministic search; do NOT pipe the whole corpus into an LLM. Only synthesize over the small returned hit set if asked.
• If the query is conceptual/"what do I think about X", prefer /ask (semantic) instead; if it's a person's name, prefer /find.

OUTPUT: A short ranked list of the best matches (title · source · date · snippet · file link) + any chat-title hits, then offer next step. End replies to Anton with a 🧒 In plain words recap.

RELATION (do not duplicate):

• /ask = semantic meaning (RAG e5+reranker) over curated vault. /find = exact person names (names.db). /search = exact words across ALL conversations (this).
• Vector/RRF/reranker lane + refresh routine: see memory unified-search-layer + decision note.

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Like this skill? It is part of second-brain-starter-kit: the second brain we built for ourselves and run every day at Palo Alto AI Research Lab. Install the whole set with npx skills add tonydzi/second-brain-starter-kit. Everything is open source and free, so take what you need.

Flagships worth a look on their own: secondop-panel (a second opinion from a panel of external models), claude-memory-tidy (stop your agent's memory from rotting), telegram-mcp-kit (your own Telegram over MCP in about 15 minutes).

Author: Anton Dziatkovskii, Palo Alto AI Research Lab. Telegram @tonydzi - WhatsApp +1 341 222 9178 - X @Tony_Stef_

Engineers: want to test-drive this setup? Message me. I hand out free starter seeds to engineers who test and report back, and custom skill requests are welcome.