Borrowing it
Nothing to install: this file belongs to mamertofabian/mcp-everything-search. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/mamertofabian/mcp-everything-search/main/.claude/skills/maid-run-review/SKILL.mdgit clone --depth 1 https://github.com/mamertofabian/mcp-everything-searchWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/mamertofabian/mcp-everything-search/maid-run-review)<a href="https://agentmods.dev/skills/mamertofabian/mcp-everything-search/maid-run-review"><img src="https://agentmods.dev/badge/skills/mamertofabian/mcp-everything-search/maid-run-review/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/mamertofabian/mcp-everything-search/maid-run-review"><img src="https://agentmods.dev/badge/skills/mamertofabian/mcp-everything-search/maid-run-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00020 | $0.00719 |
| Opus 5.5 | $0.00008 | $0.00288 |
| Sonnet 5.5 | $0.00004 | $0.00144 |
| Haiku 4.5 | $0.00002 | $0.00072 |
Grade A, and why
maid-run-review 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 11d ago.
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.
How it starts
The opening of the file, as written. The whole thing — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MAID Run Review
Generate an optional LLM-assisted review of one completed MAID run while keeping maid-runner itself deterministic and AI-free. This skill is the only place in the run-review workflow that may call a model or touch provider credentials.
Rules
- Keep runner commands deterministic. Use
maid evaluateonly for run evidence, prompt request creation, review validation, and markdown rendering. - Use the model access already available to the hosting agent, or a specific model the user chooses.
- CLOUD-PRIVACY: before using a cloud model, disclose that the bounded run
review request leaves the machine. Name what run evidence leaves the machine:
deterministic evaluation findings, plan-lock revision evidence, Outcome
lessons and review notes, incident counts, and any operator-supplied diff
file included with
--diff-file. - A non-zero review validation result is a hard stop. Do not hand-edit citations or evidence ids to satisfy validation; regenerate the candidate review from the bounded request instead.
- The final report is advisory, never a gate. It must not be fed back to the implementing agent as instructions during an active run.
- Use only registered
maid evaluateoptions. Do not invent provider, model, base URL, timeout, or API-key options for maid-runner.
Workflow
-
Show the deterministic run evidence first:
maid evaluate run <manifest> -
Build the bounded review request:
maid evaluate prompt <manifest> --output .maid/run-review-request.jsonIf the operator wants diff evidence included, prepare a diff file explicitly, say how it was prepared, and pass it through the existing option:
maid evaluate prompt <manifest> --diff-file <diff-file> --output .maid/run-review-request.json -
Generate a candidate review with an agent-available model.
Send only
.maid/run-review-request.jsonto the model. If that uses a cloud model, disclose CLOUD-PRIVACY first and name what run evidence leaves the machine. The model output must be a JSON run review that cites only evidence ids from the request.
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.
- 11d ago First seen · 85 lines · 20 tokens per session scan A 074f230dc3d7
maid-run-review is a skill published in the GitHub repository mamertofabian/mcp-everything-search (367 stars, last pushed 12d ago), licensed MIT. It adds 20 tokens to every session and 719 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-10-18.
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