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.
npx skills add hyhmrright/brooks-lint --skill brooks-auditgit 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/skills/hyhmrright/brooks-lint/brooks-audit)<a href="https://agentmods.dev/skills/hyhmrright/brooks-lint/brooks-audit"><img src="https://agentmods.dev/badge/skills/hyhmrright/brooks-lint/brooks-audit/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/hyhmrright/brooks-lint/brooks-audit"><img src="https://agentmods.dev/badge/skills/hyhmrright/brooks-lint/brooks-audit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00143 | $0.00488 |
| Opus 5 | $0.00072 | $0.00244 |
| Sonnet 5 | $0.00029 | $0.00098 |
| Haiku 4.5 | $0.00014 | $0.00049 |
Grade A, and why
brooks-audit 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 10d 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.
What it actually says
Brooks-Lint — Architecture Audit
Setup
Read in order:
../_shared/common.md— Iron Law, Project Config, Report Template, Health Score../_shared/source-coverage.md— book coverage, exceptions, tradeoffs../_shared/decay-risks.md— symptom definitions and source attributionsarchitecture-guide.md(this directory) — the audit framework
Process
Onboarding mode: If the user asks for an onboarding report, codebase tour, or
"explain this codebase to a new developer", read onboarding-guide.md from this
directory and follow it instead of architecture-guide.md. This mode explains rather
than diagnoses — no Health Score, no Iron Law findings.
Scope: if the user did not specify files or a directory, apply Auto Scope Detection
(../_shared/common.md) first.
- Gather codebase context and draw the module dependency graph as Mermaid (Steps 0–1 of the guide)
- Scan for each decay risk in the order specified (Steps 2–4 of the guide)
- Assign node colors in the Mermaid diagram based on findings (red/yellow/green) — after Step 4
- Run the Testability Seam Assessment (Step 5 of the guide)
- Run the Conway's Law check (Step 6 of the guide)
- Output using the Report Template from common.md — Mermaid graph FIRST, then Findings
Mode line in report: Architecture Audit
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 10d ago First seen · 44 lines · 143 tokens per session scan A 4fcee93c0230
brooks-audit is a skill published in the GitHub repository hyhmrright/brooks-lint (1,462 stars, last pushed 3d ago), licensed MIT. It adds 143 tokens to every session and 488 once invoked, about $0.0007 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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The depth half of a review - the dimensions a diff is read against (correctness, boundaries, concurrency, failure paths, secrets, data access, structure, test quality) and the rule that a finding is refuted before it is reported. The verdict stays with the reviewer agent. Use when reviewing a diff or a pull request…
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The optimization pass, defined - delete before you add, one smell class per pass, behaviour pinned by a test that ran BEFORE the edit. Lints a SKILL.md and prose by the same instinct. Use for the per-story optimization pass or when code has grown noisy without growing capable.
code-confidence-map
Assesses code comprehensibility and maintainability risk. Use when the user asks about code confidence, risk, maintainability, tech debt, code health, or whether code is safe to change. Also use when the user asks to analyze code quality, scan for risks, check if code is messy or complex, audit code, do a code…
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Structured code review with parallel audit agents, confidence-scored triage, and optional auto-fix. Examines uncommitted changes, staged diffs, commit ranges, or specific paths. Produces a tiered report (MUST-FIX / RECOMMENDED / NIT) backed by evidence, then optionally applies fixes with verification.