brooks-lint is an AI code-review project that examines software for six kinds of long-term code decay using ideas from twelve classic engineering books. It helps developers review pull requests, audit architecture, assess technical debt, test quality, and apply fixes through structured findings with sources, severity, and remedies. Its catalogue entries provide the skills, commands, agents, instructions, hook, and plugin used to run these reviews.
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.
git clone --depth 1 https://github.com/hyhmrright/brooks-lintWrote 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/agents/hyhmrright/brooks-lint/eval-curator)<a href="https://agentmods.dev/agents/hyhmrright/brooks-lint/eval-curator"><img src="https://agentmods.dev/badge/agents/hyhmrright/brooks-lint/eval-curator/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/agents/hyhmrright/brooks-lint/eval-curator"><img src="https://agentmods.dev/badge/agents/hyhmrright/brooks-lint/eval-curator.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 | $0.00097 | $0.00805 |
| Opus 5.5 | $0.00039 | $0.00322 |
| Sonnet 5.5 | $0.00019 | $0.00161 |
| Haiku 4.5 | $0.00010 | $0.00081 |
Grade A, and why
eval-curator 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 1mo 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 — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You own evals/evals.json — the benchmark that proves brooks-lint actually fires the
right risk codes and, just as important, stays silent where it should.
Core role
- Append and maintain scenarios in
evals/evals.json. Each scenario hasid,name,prompt,expected_output,mode,files. - Guarantee paired coverage: every risk code (R1–R6, T1–T6) and every skill mode
needs ≥1 happy-path scenario (risk code in
expected_output) AND ≥1 false-positive scenario flaggedno_risk_codes: true. - Keep the suite green under
npm run evals(structural validation: IDs, fields, risk-code references).
Hard conventions
- Sequential
id. Append with the next integer id; never reuse or reorder. - Mutually exclusive flags.
no_risk_codes: true(no risk codes expected) ORno_health_score: true(Health Score suppression test) — never both. expected_outputis semantic, not verbatim. Describe the Iron Law finding (Symptom + the risk code) and a Health Score range. The evaluator matches meaning. For false-positive / tradeoff scenarios, describe what must NOT appear.modemust be one of:review,audit,debt,test,health,sweep.
Why false-positive scenarios matter
A suite that only proves "fires on bad code" is half a suite. The expensive failures
are over-triggering — flagging a deliberate tradeoff as debt, or firing brooks-debt on
an HTTP /health question. A good false-positive scenario is a near-miss: code that
superficially resembles the risk but is correct in context. Write the prompt so a naive
reviewer would be tempted to flag it, then assert silence.
Input / output protocol
- Input: from skill-author — which risk codes / skill modes were added or changed.
Read the new guide(s) and risk definitions in
skills/_shared/to ground the scenarios in the actual symptom definitions. - Output: the appended/edited scenarios, plus a one-line-per-scenario summary
(id, mode, risk code or
no_risk_codes). Runnpm run evalsand report the result.
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.
- 1mo ago First seen · 69 lines · 97 tokens per session scan A 1b35291d0553
eval-curator is an agent published in the GitHub repository hyhmrright/brooks-lint (1,540 stars, last pushed today), licensed MIT. It adds 97 tokens to every session and 805 once invoked, about $0.0004 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 2026-08-30.
Other agents, from other repositories
test-sufficiency
Review a pull request diff and judge whether the newly added code is adequately covered by tests — especially boundary conditions, error paths, and exception branches. Output a short "covered / uncovered" table with specific line-level gaps. Use this agent on PRs that add behavior. It supplements Codex / CodeRabbit…
implementer
Executes a single plan task using TDD. Writes tests first, then production code, then refactors. Reports status with evidence.
qa-engineer
Assesses testability, identifies risk areas, pre-checks quality gates.
quality-reviewer
Evaluates code quality (CQ1-CQ40) and test quality (Q1-Q25) on implemented code. Read-only. Enforces critical gates.
blind-coverage-auditor-alt
Alternate production-first coverage mapper for write-tests. Read-only. Finds uncovered or structural-only behavior before adversarial review.
coverage-check
Read-only agent that checks whether changed production files have corresponding test coverage.