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Agentfinder

Skill theomonfort/skills/skills/agentfinder

Théo's collection of Agent Skills for GitHub Copilot

Install
npx -y skills add theomonfort/skills --skill agentfinder

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

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What its author says it does

Copied from the file, not written here

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.

SKILL.md

3.8 KB, as published. Nobody here has run it

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.

Gives 0 of the 12 instructions most mcp tooling skills give

Counted across 638 of the 750 authors here whose files we hold, read 2026-08-06

  • create ten complex read-only evaluation questionsin 71 of 638, across 17 files
  • test servers using MCP Inspectorin 60 of 638, across 18 files
  • provide actionable error messagesin 56 of 638, across 14 files
  • prioritize comprehensive API coverage over specific workflowsin 54 of 638, across 12 files
  • use TypeScript and Streamable HTTP for remote serversin 53 of 638, across 7 files
  • define structured output schemas where possiblein 51 of 638, across 9 files
  • use Zod or Pydantic for input schemasin 48 of 638, across 6 files
  • fetch MCP specification pages with markdown suffixin 46 of 638, across 4 files
  • load framework documentation using WebFetchin 45 of 638, across 3 files
  • verify each evaluation answer independentlyin 45 of 638, across 3 files
  • implement API client with authentication and paginationin 45 of 638, across 3 files
  • Define input schemas with validationin 28 of 638, across 10 files

Said here and by no other author read

  • Invoke agentfinder with a task query
  • Skip search for purely local work
  • Query GitHub Agent Finder directly
  • Never ask the user for a URL
  • Use a different service only if explicitly named
  • Send task as ARD query object

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.