Agent Skills MCP
Click on "Install 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., "@Agent Skills MCPI need a skill for local lead research"
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.
Agent Skills MCP
A remote MCP server that hands agent skills to Claude on request.
Ask "I need a skill for local lead research" and Claude calls get_skill,
reads the instructions, asks you for what the skill needs, and gets to work.
Skills live in a database rather than in the deployment, so editing one is a seed command, not a redeploy.
What it does and does not do
This server serves skills. It does not run them, and that is deliberate.
A skill needs the user's own API tokens and writes files to the user's own machine. A remote server has neither. So the split is:
Where | |
The instructions | Here, in the database |
The API token | The user's machine, never sent to this server |
Running the code | The user's agent |
The output file | The user's disk |
The server sends instructions. The agent on the other end carries them out with credentials this server never sees.
Related MCP server: cort4ex-mcp
Tools
Tool | What it does |
| Lists every skill with its description, so the agent can pick one from a topic. |
| Returns one skill's full markdown. Matching is forgiving: "local lead research", "local-lead-research" and "lead" all resolve to the same skill. |
Setup
1. Create the table. In your Supabase project, open the SQL editor and run
supabase.sql.
2. Seed it from the .SKILL files in skills/, which are the
source of truth:
cp .env.example .env && npm install && npm run seed3. Deploy to Vercel. Import the repo, then set two environment variables:
Variable | Value |
|
|
| your anon key |
The service role key stays on your machine for seeding. The server only reads, so it only needs the anon key.
4. Connect it to Claude. Settings → Connectors → Add custom connector, and paste your endpoint:
https://your-app.vercel.app/api/mcpEditing a skill
Edit the file in skills/, then:
npm run seedLive within a minute, no redeploy. Responses are cached for 60 seconds so a burst of calls hits the database once.
Adding a skill
A skill is markdown with YAML frontmatter, saved with a .SKILL extension:
---
name: my-skill
description: One sentence on what it does and when to use it. Agents read this to decide whether the skill is relevant, so it matters more than it looks.
---
# My skill
Instructions for the agent...Drop it into skills/, run the seed, done.
Local development
npm install
npm run devThe endpoint is http://localhost:3000/api/mcp. Point the MCP Inspector at it
to try the tools without a Claude connector:
npx @modelcontextprotocol/inspectorSecurity
No secrets pass through the conversation. The server holds no user credentials and has no tool that accepts one.
The database is read-only in production. Row level security allows anonymous
selectand nothing else; writes need the service role key, which never reaches the deployment.The endpoint is unauthenticated. Skills are public instructions, so anyone with the URL can read them. Put auth in front of it before storing anything you would not publish.
License
MIT
Tool Schema Changelog
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Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Connectors
Agent-first skill marketplace with USK open standard for Claude, Cursor, Gemini, Codex CLI.
Private, portable memory and reusable skills for AI agents.
- SkilderOAuthai.skilder
One place to build, share, and govern the skills and tools your AI agents use at work.
AI agent skills marketplace — token-efficient skill search & execution
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