Skillhub MCP

Skillhub MCP

Links

• PyPI: https://pypi.org/project/skillhub-mcp/
• PyPI v1.0.1: https://pypi.org/project/skillhub-mcp/1.0.1/
• Skills directory: http://skills.214140846.net/

mcp-name: io.github.214140846/skillhub-mcp

You already have Claude-style skills (SKILL.md), but in practice you often hit a wall:

• your client speaks MCP, not Claude Skills
• your team uses multiple agents (Cursor, Copilot, Codex, etc.), so skills are painful to reuse across tools
• you want a more flexible way to organize and ship skills (nested folders, zip packaging)

Skillhub MCP bridges that gap: it turns Claude-style skills into MCP tools, so any MCP client can call the same skills.

⚠️ Experimental. Skills may contain scripts/resources. Treat them as untrusted and run with sandboxes/containers when possible.

Is this an MCP server or an MCP client?

This project is an MCP server.

• Skillhub MCP (this repo): runs as a server process and exposes tools/resources to clients.
• MCP clients: editors/agents like Cursor, Claude Code, Codex, etc. They start or connect to MCP servers.

What You Get

• Cross-client reuse: install once, use from any MCP client
• Flexible packaging: nested directories, .zip and .skill archives
• Skill resources: expose scripts/datasets/examples as MCP resources (files the client can read)
• Resource fallback: a fetch_resource tool for clients without native MCP resource support
• Multiple transports: stdio (default), http, sse

Quick Start

Default skills root: ~/.skillhub-mcp

uvx (recommended)

{

"skillhub-mcp": { "command": "uvx", "args": ["skillhub-mcp@latest"] } }

Use a custom skills root:

{

"skillhub-mcp": { "command": "uvx", "args": ["skillhub-mcp@latest", "/path/to/skills"] } }

Install in Popular Editors (MCP Clients)

Below are minimal working examples for mainstream “vibe coding” editors.

Cursor

Cursor supports configuring MCP servers via mcp.json. Add the following to your global ~/.cursor/mcp.json or project .cursor/mcp.json, then restart Cursor.

{

"mcpServers": { "skillhub-mcp": { "type": "stdio", "command": "uvx", "args": ["skillhub-mcp@latest", "/path/to/skills"] } } }

Claude Code

Option A: configure via Claude Code CLI (recommended for quick setup):

claude mcp add --transport stdio skillhub-mcp -- uvx skillhub-mcp@latest /path/to/skills

Option B: project-scoped configuration via .mcp.json at your project root. You may need to explicitly allow project MCP servers in .claude/settings.json.

./.mcp.json

{

"mcpServers": { "skillhub-mcp": { "type": "stdio", "command": "uvx", "args": ["skillhub-mcp@latest", "/path/to/skills"] } } }

./.claude/settings.json (approve only this server)

{

"enabledMcpjsonServers": ["skillhub-mcp"] }

Codex (OpenAI)

Option A: use the Codex CLI to add a stdio MCP server:

codex mcp add skillhub-mcp -- uvx skillhub-mcp@latest /path/to/skills

Option B: edit ~/.codex/config.toml:

[mcp_servers.skillhub-mcp]

command = "uvx" args = ["skillhub-mcp@latest", "/path/to/skills"]

Skill Format

Skillhub MCP discovers skills under the root directory (default ~/.skillhub-mcp). Each skill can be:

• a directory containing SKILL.md
• a .zip or .skill archive containing SKILL.md (at the archive root or inside a single top-level folder)

All other files become downloadable MCP resources for your agent to read. Note: Skillhub MCP does not execute scripts; the client decides whether/how to run them.

Example layout:

~/.skillhub-mcp/

├── summarize-docs/ │ ├── SKILL.md │ ├── summarize.py │ └── prompts/example.txt ├── translate.zip ├── analyzer.skill └── web-search/ └── SKILL.md

Archive rules:

translate.zip

├── SKILL.md └── helpers/ └── translate.js

data-cleaner.zip

└── data-cleaner/ ├── SKILL.md └── clean.py

Directory Structure: Skillhub MCP vs Claude Code

Claude Code expects a flat skills directory (each immediate subdirectory is one skill). Skillhub MCP is more permissive:

• nested directories are discovered
• .zip / .skill packaged skills are supported

If you need Claude Code compatibility, keep the flat layout.

CLI Reference

skillhub-mcp [skills_root] [options]

Safety Notes

• Skills are not "just prompts": they can include scripts and arbitrary files.
• Skillhub MCP does not run scripts, but your client might. Prefer running in a sandbox/container.

Language

• English: README.md
• 中文: README.zh-CN.md

About the Author

I focus on AI SaaS going global, covering the full journey from idea validation and vibe coding to product development, infrastructure, SEO, backlinks, and growth experiments.

Everything shared here comes from real projects, real traffic, and real revenue attempts.

• Feishu Knowledge Base:

Thor’s AI Going-Global Content Planning

A structured knowledge base documenting hands-on experience in AI product overseas expansion, including demand discovery, execution strategies, and common pitfalls.

• Blog:

Thor-AI Blog

Long-form notes and case studies on building, launching, and iterating AI products in public.

• Open-source Project (High Star):

Smart Campus System

• GitHub: https://github.com/214140846/TOGO_School_Miniprograme
• Gitee: https://gitee.com/zengyunengineer/TOGO_School_Miniprograme
• Social:

Jike

Sharing real-time thoughts on indie hacking, AI tools, and product growth.

• Product:
• AI Video Generation Platform:

Sora 2 Sora 2 ai

An online platform for AI-powered video generation, focused on practical use cases and real user workflows.

• AI Video & Image Generation:

AI Video & Image Collection

Model pages:

• Grok Video
• Sora 2
• Veo 3.1
• Veo 3
• Veo 2
• Kling 2.6
• Wan 2.5
• Seedance
• Nano Banana 2
• Nano Banana Pro

A curated collection of AI video and image generation tools, experiments, and capability tracking.

• AI Video & Image Collection:

https://www.notion.so/2e7600937cb3808c818efe79141f7ee6