name	agentfinder
description	Discover installable MCP servers, tools, skills, and agents for a task by searching an ARD Agent Finder. Use whenever the user wants to find or install a tool, MCP server, skill, agent, or integration for something they are trying to do — email, calendars, databases, payments, cloud platforms, CI/CD, messaging, monitoring, file storage, and similar services.
argument-hint	<what you want to find>

Find agentic resources (Agent Finder)

Use this skill when the user asks you to find an MCP server, tool, skill, or agent for a task. It searches an ARD Agent Finder (a discovery service) and presents matches for the user to choose from.

Invoke it as /agentfinder <query>, where <query> is the task to find tools for. Also use it whenever the user otherwise asks you to find a tool, MCP server, or integration for a task. Search the registry when the task needs a third-party service (email, calendars, payments, databases, cloud, CI/CD, monitoring, messaging, file storage); skip it for purely local work (writing code, editing files, git, shell, math).

1. Use GitHub's Agent Finder (built in)

This skill already knows where to search — GitHub's Agent Finder:

https://agentfinder.github.com/api/v1/search

Query it directly. Never ask the user for a URL — the endpoint is built in, so /agentfinder <task> works with zero configuration. No authentication is required.

Use a different service only if the user explicitly names one (e.g. Hugging Face Discover, or one from their agent-finders.json). If they give an ARD service base URL (a version root like https://host/api/v1), derive the endpoints from it: append /search to search, /mcp for its MCP endpoint.

2. Query it

Send the user's task as an ARD query object. Use whatever HTTP capability you have (in a terminal, curl):

curl -s https://agentfinder.github.com/api/v1/search \
  -H 'Content-Type: application/json' \
  -d '{"query":{"text":"<the user's task, in plain language>"}}'

• The body is the ARD spec shape: a query object with a text field. Add an optional query.filter (e.g. {"type":["application/mcp-server+json"]}) to narrow by resource type, and "pageSize": <n> to cap results.

3. Present the results

The response is { "results": [ ... ] }. Each result has displayName, mediaType (the resource type, e.g. application/mcp-server+json), url, identifier, source, and a relevance score. Show a numbered list — for each: displayName, the type, the url, and the score. State that the score is relevance only — not a trust or safety rating.

4. Never auto-install

Do not add, enable, connect, or install any returned resource yourself. Installation is always the user's explicit choice.

5. Install only on request

Once the user picks a result, show them how to add that resource using its url:

• application/mcp-server+json — add it as an MCP server (a .vscode/mcp.json or claude_desktop_config.json entry, or your client's "add MCP server" flow), pointed at the resource's url.
• application/ai-skill — install the skill from its url.
• otherwise — connect to it at its url over its own protocol.

Then stop and let the user act.

Installation

GitHub Copilot — copy this github-copilot/ folder into a directory Copilot scans: ~/.copilot/skills/ (personal) or .github/skills/ (project). Copilot also reads ~/.claude/skills/, so a copy there is picked up too.

cp -r connectors/skills/github-copilot ~/.copilot/skills/

Then invoke /agentfinder <query>.

This skill defaults to GitHub's Agent Finder with no configuration. For a connector that asks which discovery service to use instead, see the generic agentfinder skill.