skillsearch-mcp

Semantic skill-library search as an MCP server. Given a natural-language query, it finds the most relevant skills (or any Markdown knowledge files) in your local skill directories — works out of the box with Claude Code, Codex, Cursor, and any MCP client.

Built on a small, local-first stack:

Embeddings: bge-small-zh-v1.5 via Ollama (works for Chinese + English)
Storage: SQLite (WAL) — vector blobs + keyword columns, no heavy vector DB
Ranking: dual-channel Reciprocal Rank Fusion (cosine + substring keywords), so both semantic intent and exact terms like ffmpeg or 14234 are found

user query ──▶ embed (bge-small-zh) ──┐
              keyword grams (4/2-gram) ─┴─▶ RRF ──▶ top-k skills + paths

Why

Claude Code and Codex already have filesystem access. What they lack is a fast way to answer "which of my 100+ skills/knowledge files matches this task?". This server turns your skill library into a searchable index that any MCP client can query in ~100ms (local Ollama).

Requirements

Python 3.10+
Ollama running locally with the embedding model:

ollama pull quentinz/bge-small-zh-v1.5

Install

pip install skillsearch-mcp        # from PyPI (once published)
# or from source
git clone https://github.com/<you>/skillsearch-mcp.git
cd skillsearch-mcp && pip install -e .

Configuration

Environment variables:

Variable	Default	Description
`SKILLSEARCH_SKILLS_DIR`	`~/skills`	Skill/knowledge root(s). Multiple dirs separated by `;` (Windows) or `:` (Unix). Scanned recursively for `SKILL.md` files.
`SKILLSEARCH_DB`	`~/.cache/skillsearch/vector_index.db`	Where the index lives. Rebuilds automatically when skills change (mtime-based).
`OLLAMA_URL`	`http://127.0.0.1:11434`	Ollama endpoint.
`EMBED_MODEL`	`quentinz/bge-small-zh-v1.5`	Embedding model. Any Ollama embedding model works, but index must be rebuilt after changing it (server does this automatically via meta check).

Client setup

Claude Code

claude mcp add skillsearch -- python -m skillsearch_mcp

or in .mcp.json:

{
  "mcpServers": {
    "skillsearch": {
      "command": "python",
      "args": ["-m", "skillsearch_mcp"],
      "env": { "SKILLSEARCH_SKILLS_DIR": "/path/to/skills" }
    }
  }
}

Codex

codex mcp add skillsearch -- python -m skillsearch_mcp

or ~/.codex/config.toml:

[mcp_servers.skillsearch]
command = "python"
args = ["-m", "skillsearch_mcp"]
env = { SKILLSEARCH_SKILLS_DIR = "/path/to/skills" }

Tools

`skill_search(query, k=5)`

Semantic search. Returns top-k matching skills with name, score, description, and absolute path. k is capped at 10.

`skill_list()`

List every indexed skill (name + description + path). Useful for exploration or for clients that want a full inventory.

Skill file format

Any directory containing SKILL.md files is indexed. A minimal example:

---
name: ffmpeg-video-batch-processing
description: ffmpeg video batch processing — watermark, compress, rename
---

# ffmpeg video batch processing
...full content...

name and description from the frontmatter are used for search. Files under .archive, .git, references, assets, templates, node_modules are skipped.

How indexing works

On first query the server scans SKILLSEARCH_SKILLS_DIR, embeds the first 512 chars of each skill, and stores vectors in SQLite (WAL).
On subsequent queries it only re-indexes files whose mtime changed (cheap update_index pass), and prunes deleted ones.
Query path: embed the query with the retrieval prefix, cosine-search the vector table, run 4-gram/2-gram substring scoring over name/description/ full_text, then fuse both rankings with RRF.

License

MIT