skillhub-mcp
MCP server that exposes Claude-style skills to any MCP client.
MCP 服务配置
复制以下 JSON 到 OPClaw 或其他 MCP 客户端的配置文件中即可使用
{
"mcpServers": {
"skillhub-mcp": {
"args": [
"skillhub-mcp@1.0.5"
],
"command": "uvx"
}
}
}
可用工具 (5 个)
该服务在 MCP 协议中暴露的工具,AI 可按需调用
tavily_search 14 个参数 需填 1 项
Search the web for current information on any topic. Use for news, facts, or data beyond your knowledge cutoff. Returns snippets and source URLs.
必填参数:query
tavily_extract 6 个参数 需填 1 项
Extract content from URLs. Returns raw page content in markdown or text format.
必填参数:urls
tavily_crawl 11 个参数 需填 1 项
Crawl a website starting from a URL. Extracts content from pages with configurable depth and breadth.
必填参数:url
tavily_map 8 个参数 需填 1 项
Map a website's structure. Returns a list of URLs found starting from the base URL.
必填参数:url
tavily_research 2 个参数 需填 1 项
Perform comprehensive research on a given topic or question. Use this tool when you need to gather information from multiple sources to answer a question or complete a task. Returns a detailed response based on the research findings.
必填参数:input
服务介绍
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,
.zipand.skillarchives - Skill resources: expose scripts/datasets/examples as MCP resources (files the client can read)
- Resource fallback: a
fetch_resourcetool 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
.zipor.skillarchive containingSKILL.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/.skillpackaged skills are supported
If you need Claude Code compatibility, keep the flat layout.
# CLI Reference
skillhub-mcp [skills_root] [options]
| Flag / Option | Description |
| - -- | - -- |
| positional skills_root | Optional skills directory (defaults to ~/.skillhub-mcp). |
| - -transport {stdio,http,sse} | Transport (default stdio). |
| - -host HOST | Bind address for HTTP/SSE transports. |
| - -port PORT | Port for HTTP/SSE transports. |
| - -path PATH | URL path for HTTP transport. |
| - -list-skills | List discovered skills and exit. |
| - -verbose | Emit debug logging. |
| - -log | Mirror verbose logs to /tmp/skillhub-mcp.log. |
# 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:
Long-form notes and case studies on building, launching, and iterating AI products in public.
-
Open-source Project (High Star):
Smart Campus System
-
Social:
Sharing real-time thoughts on indie hacking, AI tools, and product growth.
-
Product:
-
AI Video Generation Platform:
An online platform for AI-powered video generation, focused on practical use cases and real user workflows.
-
AI Video & Image Generation:
Model pages:
-
A curated collection of AI video and image generation tools, experiments, and capability tracking.
-
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AI Video & Image Collection: