agentsclimarketplace

Implement eval

Skill PrefectHQ/prefect-mcp-server/.agents/skills/implement-eval

Implement or update scenario-focused evaluations for the Prefect MCP server. Use when adding eval coverage from a GitHub issue or a described support scenario, preparing Prefect server state, prompting the test agent, asserting user-facing behavior, running evals, and updating the eval catalog.From its SKILL.md

Install
npx -y skills add PrefectHQ/prefect-mcp-server --skill implement-eval

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

SKILL.md

1.6 KB, as published. Nobody here has run it

Implement a Prefect MCP eval

  1. Read AGENTS.md and evals/README.md before making changes.
  2. Resolve the requested scenario:
    • If the user provides a GitHub issue number or URL, read it with an available GitHub integration or the gh CLI.
    • Otherwise, use the scenario described by the user.
    • Identify the user-facing question, required Prefect state, expected investigation, and success criteria.
  3. Inspect related evals and fixtures before choosing a file location. Extend an existing scenario directory when appropriate; otherwise add a focused test under evals/.
  4. Implement the eval:
    • Create server state in a fixture.
    • Prompt the agent in language a Prefect user or support engineer would use.
    • Assert on the final behavior or answer, not incidental wording or private implementation details.
    • Keep protocol behavior in unit tests rather than evals.
  5. Run the narrowest relevant eval first, then run the full suite with just evals.
  6. Add or update the eval's row in evals/README.md.
  7. Review the diff for unrelated changes and report the scenario covered and verification performed.

Do not add live credentials, harness-specific argument placeholders, or client-specific tool syntax to the skill.

Keep looking

Skills are one crate of 627,801. 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.