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skill-retrieval-mcp

by JayCheng113
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skill-retrieval-mcp

Your agent doesn't need you to find the right skills. It needs to search 89K of them on its own.

An MCP server that gives AI agents on-demand access to 89K+ skills covering virtually every technical domain. The agent searches as it works — the same way you look up docs mid-task.

Works with Claude Code, Codex CLI, Gemini CLI, Cursor, and any MCP-compatible agent.

The Problem

You give your agent a skill — "always use TDD," "follow this API style" — and it works. But manually installing skills doesn't scale:

  • You don't know what exists. There are thousands of skills out there. You install the 10 you happen to find — everything else, the agent guesses.

  • You can't install what you can't name. Mid-task, the agent needs a skill for "OIDC-based PyPI publishing" — but you'd never think to install that in advance.

  • 89K skills can't live in ~/.claude/skills/. Even with lazy loading, thousands of skill descriptions bloat the system prompt.

Related MCP server: skills-master-mcp

The Fix

Don't install skills upfront. Search them at runtime.

You: "Help me set up CI/CD for this Python project"

─── Step 1: Agent searches ───────────────────────────────────────────

Agent: search_skills("github actions python CI pipeline")        ← 3ms
     → 5 results (summaries only, no full instructions):
       1. "github-actions-python"    (0.91) - CI/CD pipelines for Python with pytest and linting
       2. "github-actions-docker"    (0.72) - Docker build and push in GitHub Actions
       3. "gitlab-ci-python"         (0.68) - GitLab CI/CD for Python projects
       4. "circleci-python"          (0.61) - CircleCI configuration for Python
       5. "jenkins-pipeline"         (0.45) - Jenkins declarative pipelines

─── Step 2: Agent reads descriptions, picks #1 ──────────────────────

Agent: get_skill("github-actions-python")
     → gets full guide: step-by-step setup, matrix testing, caching, best practices
     → writes .github/workflows/ci.yml

─── Step 3: New need emerges mid-task ────────────────────────────────

Agent: # workflow needs PyPI publishing — search again with different query
       search_skills("pypi publish trusted publisher")           ← 2ms
     → "pypi-trusted-publishing" (0.87) - OIDC-based PyPI publishing without API keys
     → reads guide, adds publish step

Key behaviors:

  • Search returns summaries, not full instructions — the agent reads descriptions and scores to decide which skills are worth fetching. 5 results searched, 1 skill read → 80% token savings.

  • The agent searches multiple times as the task evolves. Different phase → different query → different skill.

  • Queries are shaped by context. The second search includes "trusted publisher" — a term the agent picked up while working, not something the user said.

89K skills. < 5ms search. Zero LLM calls. Runs locally.

Installing skills manually

skill-retrieval-mcp

Scale

Dozens, if you're diligent

89K+

Discovery

You find and install each one

Agent searches by need

Selection

You pick upfront

Agent picks per-task

Search

Name matching on descriptions

Semantic, < 5ms, local FAISS

Quick Start

Three commands. Takes about 2 minutes (mostly download time).

# 1. Install
pip install "skill-retrieval-mcp[local,hf]"

# 2. Download 89K skills + pre-built vector index
skill-mcp pull --include-index

# 3. Register with your agent (auto-detects Claude Code, Cursor, etc.)
skill-mcp init

Done. Your agent now searches 89K skills on demand.

Agent

Config file

Add this

Claude Code

.mcp.json

{"mcpServers": {"skill-retrieval": {"command": "skill-mcp", "args": ["serve"]}}}

Gemini CLI

~/.gemini/settings.json

same as above

Cursor

.cursor/mcp.json

same as above

Codex CLI

~/.codex/config.toml

[mcp_servers.skill-retrieval]command = "skill-mcp"args = ["serve"]

What's In the Knowledge Base

89,267 skills across every major technical domain, sourced from LangSkills, SkillNet, Anthropic official, and community contributions.

Each skill is a structured best-practice guide — not a one-liner, but a step-by-step how-to with code examples, common pitfalls, and recommendations.

Run skill-mcp status to see what you have locally, or use list_categories to browse domains.

Tools

Tool

What it does

search_skills

Semantic search — describe what you need in natural language

keyword_search

Exact match — tool names, error messages, CLI commands

get_skill

Fetch full instructions (call after search)

list_categories

Browse available domains and counts

Search returns summaries only (saves tokens). The agent calls get_skill for the ones it actually needs.

Add Your Own Skills

<!-- ~/my-skills/deploy-checklist/SKILL.md -->
---
name: "deploy-checklist"
description: "Pre-deployment verification checklist for production releases"
tags: ["deployment", "production", "checklist"]
---

## Steps

1. Run full test suite...
2. Check database migrations...
skill-mcp import --source directory --path ~/my-skills/
# index is updated automatically — new skills are searchable immediately

No manual build-index needed. The import detects your existing index and incrementally adds only the new skills. Use --no-index to skip this (e.g. when batch-importing from multiple sources).

Custom skills merge with the pre-built ones. Deduplication is automatic.

Embedding Backends

Default: sentence-transformers/all-MiniLM-L6-v2 — local, free, no API key. Pre-built index included.

Backend

Pre-built index

Requires

sentence-transformers (default)

137MB

Nothing

openai

1.1GB

OPENAI_API_KEY

ollama

build locally

Ollama running

# Switch to OpenAI embeddings:
# 1. Edit ~/.skill-mcp/config.yaml (set backend: openai, model: text-embedding-3-large)
# 2. Download matching index:
skill-mcp pull --include-index

CLI Reference

skill-mcp init [--no-register]               Setup + register with agents
skill-mcp pull [--replace] [--include-index]  Download skills from HuggingFace
skill-mcp import --source SOURCE --path PATH  Import custom skills
skill-mcp build-index [--backend B] [--force] Build/update vector index
skill-mcp serve [--transport stdio|sse]       Start MCP server
skill-mcp search QUERY [--k N]               Test search from terminal
skill-mcp status                              Show what's loaded
skill-mcp dedup                               Remove duplicates

All commands support --data-dir DIR or env SKILL_MCP_DATA_DIR.

Development

git clone https://github.com/JayCheng113/skill-retrieval-mcp
cd skill-retrieval-mcp
pip install -e ".[all,dev]"
pytest tests/ -v    # 139 tests, ~0.7s

Architecture and design decisions: dev.md

License

MIT

This server cannot be installed

A
license - permissive license
-
quality - not tested
C
maintenance

How are these scores calculated?

Maintenance

–Maintainers
–Response time
0dRelease cycle
2Releases (12mo)
Commit activity

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