skillfinder-mcp
Searches and retrieves skills from a GitHub repository, with automatic caching and background sync to keep the index up-to-date.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@skillfinder-mcp search for skills on React hooks"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
SkillFinder MCP
SkillFinder MCP is an MCP (Model Context Protocol) server that provides on-demand skill discovery for AI coding agents.
Instead of loading a full skill library into context up front, it lets an agent query only what is relevant, pulls skills from GitHub, and returns full SKILL.md content for immediate use.
On startup, the server hydrates from local cache immediately (if present), then checks upstream SHA in the background and refreshes automatically when needed.
During package installation, SkillFinder also attempts a one-time cache prewarm so the first MCP query is faster.
Why Use It
Reduces context bloat by loading only relevant skills.
Uses fast local BM25 ranking over skill metadata.
Auto-syncs from the upstream repository when content changes.
Runs over stdio with no database, embeddings, or external service.
Related MCP server: skillsmp-mcp-server
Features
Fast BM25 search over skill name, description, and tags.
Immediate cache hydration with background SHA validation.
Duplicate skill handling with preference for canonical
skills/paths.Local disk cache for fast warm starts.
Automatic zip-archive fallback when GitHub API is unavailable/rate-limited.
Tools Exposed
search_skills(query: string, limit?: number)Returns the most relevant skills and fullSKILL.mdcontent.refresh_index()Forces a full index rebuild from GitHub (bypasses cache check).
Installation and Usage
SkillFinder MCP runs via stdio and is typically launched with npx.
Claude Desktop
Config file:
macOS:
~/Library/Application Support/Claude/claude_desktop_config.jsonWindows:
%APPDATA%\Claude\claude_desktop_config.json
{
"mcpServers": {
"skillfinder": {
"command": "npx",
"args": ["-y", "skillfinder-mcp"]
}
}
}Cursor
In Cursor: Features -> MCP Servers -> Add New MCP Server
Type:
commandName:
skillfinderCommand:
npx -y skillfinder-mcp
Claude Code
claude mcp add skillfinder -- npx -y skillfinder-mcpConfiguration
Set optional environment variables in your MCP server config:
Variable | Description | Default |
| GitHub repository containing skills ( |
|
| Default number of search results ( |
|
| Optional GitHub token to increase API limit | None |
| Parallel raw file fetch count during full index build ( |
|
| Per-request timeout in milliseconds ( |
|
| Enable/disable install-time cache prewarm ( | Enabled |
Local Development
npm install
npm run build
npm startRun the automated test suite:
npm testRun integration test:
node test-run.mjsOperational Notes
Cache location:
macOS:
~/Library/Caches/skillfinder-mcp/index.jsonLinux:
$XDG_CACHE_HOME/skillfinder-mcp/index.json(or~/.cache/...)Windows:
%LOCALAPPDATA%\skillfinder-mcp\index.json
If GitHub is temporarily unavailable, stale cache is used when present.
Install-time prewarm does not fail installation if GitHub is unavailable.
First run can take longer due to full index fetch (often 10-30s depending on network).
Warm starts are typically near-instant because results are served from cache.
License
MIT. See LICENSE.
Available Tools
2 toolsrefresh_indexA
Force a full re-fetch of the skill index from GitHub, bypassing the cache. Use this if you know the upstream skill repo was recently updated.
Inputs: no named parameters.
search_skillsA
Search the skill library for skills relevant to your current task. Pass a natural language description of what you're working on and get back the most relevant skill documents with full content. Call this BEFORE starting any coding task to get specialized guidance.
Inputs: query (required), limit.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 2 tools
search_skills and refresh_index serve clearly different purposes—one retrieves relevant skill documents, the other updates the underlying index. There is no overlap or ambiguity between them.
Both tool names follow a consistent verb_noun snake_case pattern (search_skills, refresh_index). The verbs clearly describe the action and the nouns identify the target.
Two tools is slightly below the typical 3-15 range, but for a focused skill-finding server the pair is logical: one core search tool and one maintenance tool. It is not thin enough to feel incomplete.
The domain is read-only skill discovery, and the search tool returns full skill content while refresh_index keeps the library current. No obvious operations are missing for this server's stated purpose.
Maintenance
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