Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/prefecthq/prefect-mcp-server/implement-evalnpx skills add PrefectHQ/prefect-mcp-server --skill implement-evalgit clone --depth 1 https://github.com/PrefectHQ/prefect-mcp-serverWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00059 | $0.00332 |
| Opus 5 | $0.00030 | $0.00166 |
| Sonnet 5 | $0.00012 | $0.00066 |
| Haiku 4.5 | $0.00006 | $0.00033 |
Grade A, and why
implement-eval scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Implement a Prefect MCP eval
- Read
AGENTS.mdandevals/README.mdbefore making changes. - Resolve the requested scenario:
- If the user provides a GitHub issue number or URL, read it with an available GitHub integration or the
ghCLI. - Otherwise, use the scenario described by the user.
- Identify the user-facing question, required Prefect state, expected investigation, and success criteria.
- If the user provides a GitHub issue number or URL, read it with an available GitHub integration or the
- Inspect related evals and fixtures before choosing a file location. Extend an existing scenario directory when appropriate; otherwise add a focused test under
evals/. - 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.
- Run the narrowest relevant eval first, then run the full suite with
just evals. - Add or update the eval's row in
evals/README.md. - 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.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 24 lines · 59 tokens per session scan A 4feb62410368
implement-eval is a skill published in the GitHub repository PrefectHQ/prefect-mcp-server (52 stars, last pushed 16d ago), licensed MIT. It adds 59 tokens to every session and 332 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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