source-search

name

source-search

description

Search the Mnemon knowledge library (Knowledge vault) for sources, extracts, and synthesis notes. Use whenever the user asks "what do I know about X", "find sources about", "search knowledge", "have I saved anything about", references a topic they might have captured, or when you need to check for prior context before answering a substantive question. Prefer this over manual Grep on the vault — it handles tokenization, tier selection, domain filtering, and rating-based ranking in one call. Hybrid by default: keyword (rating-ranked) plus qmd vector-semantic recall that surfaces conceptually-related notes even without keyword overlap, returned as two blocks; the semantic layer is fail-open and degrades to keyword-only if qmd is unavailable.

/source-search — Search the Mnemon Knowledge Library

Searches the Knowledge vault for sources matching a query. Hybrid by default: a keyword layer (rating-ranked) and a qmd vector-semantic layer (score-ranked) run together and come back as two blocks — results (keyword) and semantic.results (meaning-based). The semantic layer is a strict enhancement: it requires search_provider: qmd in mnemon.yaml and is fully fail-open — if qmd is missing, broken, or its index is empty, the search returns keyword results and reports semantic.available: false with a reason. You never need to handle a crash.

How to run

All the logic lives in a bundled Python script. Call it via Bash:

python3 "${CLAUDE_PLUGIN_ROOT}/skills/source-search/scripts/search.py" \
  --query "<query>" \
  [--engine auto|keyword|semantic|deep] \
  [--domain <domain>] \
  [--limit <n>] \
  [--include-source] \
  [--include-synthesis]

The script prints a JSON object to stdout. Parse it and format the results for the user (see "Output format" below).

Parameters

Flag	Default	Purpose
--query	required	The search query. The keyword layer tokenizes it into lowercase words of ≥2 chars and matches AND across tokens (a file must contain all of them). The semantic layer uses the raw query string for vector recall.
--engine	auto	auto = keyword + fast vector semantic (both blocks). keyword = keyword only (no qmd call). semantic = vector only (no keyword block). deep = keyword + qmd hybrid query with LLM expansion + rerank (higher recall, ~10s slower). See "Choosing the engine".
--domain	none	Filter by one domain tag from the extract frontmatter, e.g. career, learning, mc/ai. Applies to the keyword block.
--limit	10	Max number of results to return.
--include-source	off	Also search source.md (raw captured content), not just extract.md. Use when the user is looking for a specific phrase that might only appear in the raw source.
--include-synthesis	off	Also search the Synthesis/ directory (Dima's own notes). Use when the user might be looking for their own writing, not just captured material.
--human	off	Print human-readable output instead of JSON — only for manual CLI testing, don't pass this from the skill.

Choosing the engine

Default to auto — it almost always does the right thing (a deterministic keyword floor plus fast semantic recall). Override only when the query shape clearly calls for it:

• Leave it auto for ordinary "what do I know about X" / "find sources about Y" lookups.
• --engine semantic when the user asks conceptually and exact words are unlikely to match — "что я думал про то, как агенты учатся из опыта", "anything about models improving themselves". Vector recall finds notes that never use the user's phrasing.
• --engine keyword for precise lookups where you want only literal matches — a known title, a person's name, a quoted phrase, an --domain filter sweep. Also use it if you must avoid the qmd call entirely.
• --engine deep only when auto came back thin and the topic matters — it runs qmd's hybrid query (LLM query-expansion + reranking) for higher recall, at ~10s extra latency. Don't reach for it by default.

You don't need to pre-check whether qmd is healthy — just request the engine you want. If qmd can't serve it, the script falls back to keyword and tells you via semantic.available/reason (see below). The choice is yours to make per query; the script guarantees it never fails because of qmd.

Multi-word queries

Pass the whole query as a single --query argument. The script tokenizes it and matches by AND (all tokens must appear in the same file). You do NOT need to construct separate grep calls. Examples:

• --query "karpathy knowledge bases" → finds files containing karpathy AND knowledge AND bases
• --query "organizational design" → finds files containing both words
• --query "AI agents" → same

Fallback behavior

If zero files match all tokens (AND intersection is empty), the script automatically relaxes to the most specific token that actually exists in the corpus — the one with the smallest nonzero match count — and returns those results with "fallback_applied": true and "fallback_token": "<token>" in the JSON. If none of the tokens appear in any file, fallback_applied stays false and count is 0.

Tell the user when fallback fires, using the fallback_token value from the JSON:

"No file matched all of your search terms. Showing results for <fallback_token> only — the most specific term I could find in the library."

Do not guess which token was used — read it from the JSON.

Zero results

If count == 0 even after fallback, report: "No sources found for ''. Try broader terms or use /source-status to see what's in the library."

Output format

The script returns JSON shaped like:

{
  "query": "reinforcement learning",
  "tokens": ["reinforcement", "learning"],
  "engine": "auto",
  "fallback_applied": false,
  "fallback_token": null,
  "count": 2,
  "results": [
    {
      "path": "Sources/2026-06-14_bbe4be4a/",
      "file": "extract.md",
      "tier": "extract",
      "title": "RL Environments and how to build them (Unsloth × NVIDIA)",
      "author": "Unsloth × NVIDIA",
      "url": "https://unsloth.ai/blog/rl-environments",
      "domains": ["mc/ai", "mc/engineering", "learning"],
      "tags": ["reinforcement-learning", "rlvr", "grpo"],
      "rating": 8,
      "created": "2026-06-14",
      "snippet": "tags: [reinforcement-learning, rl-environments, rlvr, grpo]..."
    }
  ],
  "semantic": {
    "available": true,
    "reason": null,
    "index_stale_days": null,
    "count": 1,
    "results": [
      {
        "path": "Sources/2026-05-31_b6552fcb/",
        "file": "extract.md",
        "tier": "extract",
        "title": "Decision making with data — Sergey Levine",
        "author": "Thinking About Thinking",
        "url": "https://www.youtube.com/watch?v=...",
        "domains": ["mc/ai", "mc/engineering"],
        "tags": ["offline-rl", "autonomous-agents"],
        "rating": 7,
        "created": "2026-05-31",
        "snippet": "machine learning reinforcement learning ... deep learning methods",
        "engine": "semantic",
        "score": 0.68
      }
    ]
  }
}

results is the keyword block (rating-ranked); semantic.results is the vector block (score-ranked), already de-duplicated against the keyword block so it shows only what meaning-based recall adds. Semantic results carry an extra engine: "semantic" and a score (0–1).

Format it for the user with the two blocks kept visually distinct:

Found 2 results for "reinforcement learning":

1. **RL Environments and how to build them (Unsloth × NVIDIA)** · rating 8
   by Unsloth × NVIDIA · 2026-06-14 · domains: mc/ai, mc/engineering, learning
   https://unsloth.ai/blog/rl-environments
   → tags: [reinforcement-learning, rlvr, grpo]...
   path: Sources/2026-06-14_bbe4be4a/

Semantically related (also worth a look):

1. **Decision making with data — Sergey Levine** · rating 7 · score 0.68
   by Thinking About Thinking · 2026-05-31 · domains: mc/ai, mc/engineering
   → machine learning reinforcement learning ... deep learning methods
   path: Sources/2026-05-31_b6552fcb/

Lead with title + rating — rating is Dima's primary triage signal, so it should be visible immediately. Include the wikilink-friendly path at the end so he can jump to the note. Show score on semantic results so he can see confidence.

Handling the semantic block:

• semantic.available: true with results → render the "Semantically related" section. Skip the section entirely if it's empty (don't print an empty header).
• semantic.index_stale_days > 7 → add a one-line note: "(semantic index is N days stale — run qmd update && qmd embed to include recent captures)". Recent notes may be missing from vector recall until then.
• semantic.available: false → just present the keyword results. Only mention the reason if the user explicitly asked for semantic/conceptual search (e.g. they chose --engine semantic): then say "Semantic search is unavailable (<reason>), so these are keyword matches only." For ordinary auto lookups, stay silent about it — keyword results are the answer.

When to broaden the search

If the first pass returns few or zero results and the user seemed to expect more, try:

1. Re-run with --engine semantic (or deep) — meaning-based recall catches notes that don't share the user's wording. This is the first thing to try for conceptual topics.
2. Re-run with --include-source — the phrase might be in the raw capture, not the summary
3. Re-run with --include-synthesis — Dima might have written about it himself
4. Re-run with a simpler / broader query — strip modifiers, keep the topic word

Don't do all of these silently — tell the user what you're broadening and why.

What this skill does not do

• Write anything to the vault. This is a read-only skill. To add a source, use /source-add.
• Read full extract content. Results include a snippet and metadata. If the user wants to read a full extract, use the Read tool on the returned path.
• Cross-vault federation. Only searches the Knowledge vault (whatever vault_path points to in ~/Mnemon/mnemon.yaml). Multi-vault search is a v3 concern — see Projects/Mnemon/specs/v2-learnings.md Thread 3.
• Reader-Context framing. Results are raw metadata + snippets, not personalized narrative. See v2-learnings Thread 2.