Joranhonig Grimoire Grimoire

Librarian Library Search

Perform semantic search across indexed library content via a local Qdrant vector database.

When to Use

This skill is the librarian agent's primary method for consulting local knowledge bases. Use it before falling back to grep-based file search.

Semantic search finds relevant content even when exact keywords don't match — a query for "flash loan price manipulation" will surface content about oracle attacks and sandwich attacks that grep would miss.

How to Run

The search script is at skills/librarian-library-search/scripts/search.py inside the plugin directory.

uv run /path/to/grimoire/skills/librarian-library-search/scripts/search.py \
    "your natural language query here"

Flags

Examples

# Broad search across all libraries
uv run .../search.py "reentrancy vulnerability in pull-payment pattern"

# Scoped to a single library
uv run .../search.py "access control bypass" --library smart-contract-vulnerabilities

# More results
uv run .../search.py "ERC-4626 share inflation" --limit 10

Output Format

The script prints a JSON array to stdout. Each element has:

{
  "score": 0.82,
  "content": "[library-name] path/to/file\n\n...chunk text...",
  "metadata": {
    "library": "smart-contract-vulnerabilities",
    "file": "vulnerabilities/reentrancy.md",
    "chunk_idx": 2,
    "source_url": "git@github.com:kadenzipfel/smart-contract-vulnerabilities.git"
  }
}

• score — cosine similarity (0–1). Results above 0.6 are typically relevant.
• content — the chunk text, prefixed with library name and file path.
• metadata.file and metadata.source_url — use these to construct navigable GitHub URLs for citations.

Handling Failures

• "Qdrant database not found" — the index hasn't been built. Tell the user to run librarian-index and fall back to grep for now.
• "Collection not found" — same as above; the collection is created during indexing.
• No results or all scores below 0.5 — the query may not match indexed content. Fall back to grep-based search of the library files.

Guidelines

• Limit to 1–2 calls per research question. Reformulate the query if the first attempt returns poor results rather than making many calls.
• Do not use for indexing. This skill is read-only. Use librarian-index to build or rebuild the index.
• Same embedding model required. The search script must use the same FastEmbed model that was used during indexing (default: sentence-transformers/all-MiniLM-L6-v2). Do not change --embedding_model unless you also re-indexed with that model.

Source: JoranHonig/grimoire — distributed by TomeVault.