brooks-lint

brooks-lint

AI code reviews grounded in twelve classic engineering books.
Consistent. Traceable. Actionable.

English · 简体中文 · 繁體中文 · 日本語 · 한국어 · Español

Quick Start • The Six Decay Risks • What It Looks Like • Benchmark • Installation

→ Visit the website

"The bearing of a child takes nine months, no matter how many women are assigned." — Frederick Brooks, The Mythical Man-Month (1975)

50 years later, Brooks was still right — and so were McConnell, Fowler, Martin, Hunt & Thomas, Evans, Ousterhout, Winters, Meszaros, Osherove, Feathers, and the Google Testing team.

Most code quality tools count lines and cyclomatic complexity. brooks-lint goes deeper — it diagnoses your code against six decay risk dimensions synthesized from twelve classic engineering books, producing structured findings with book citations, severity labels, and concrete remedies every time.

For the full source-to-skill mapping, including exceptions and false-positive guards, see skills/_shared/source-coverage.md.

Quick Start

# Claude Code
/plugin marketplace add hyhmrright/brooks-lint
/plugin install brooks-lint@brooks-lint-marketplace

# Any other Agent Skills platform — Cursor · Codex · Gemini · Copilot · Windsurf · OpenCode · Kiro · …
curl -fsSL https://raw.githubusercontent.com/hyhmrright/brooks-lint/main/scripts/install.sh | bash -s -- <platform>

Then just ask ("review this PR", "audit the architecture"), or run one of the six commands — /brooks-review, /brooks-audit, /brooks-debt, /brooks-test, /brooks-health, /brooks-sweep (what each one does).

Every finding comes back as Symptom → Source → Consequence → Remedy with a book citation and a 0–100 Health Score. Full install options (8 more platforms) and CI/CD setup are below.

The Twelve Books

The Six Decay Risks

brooks-lint evaluates your code across six production-code decay risks and six test-suite decay risks synthesized from twelve classic engineering books:

Philosophy of SD = A Philosophy of Software Design (Ousterhout) · SE@Google = Software Engineering at Google (Winters et al.)

What It Looks Like

Given this code:

class UserService:
    def update_profile(self, user_id, name, email, avatar_url):
        user = self.db.query(f"SELECT * FROM users WHERE id = {user_id}")
        user['email'] = email
        ...
        if user['email'] != email:   # always False — silent bug
            self.smtp.send(...)
        points = user['login_count'] * 10 + 500
        self.db.execute(f"UPDATE loyalty SET points={points} WHERE user_id={user_id}")

brooks-lint produces:

Health Score: 28/100

This method concentrates four unrelated business responsibilities into a single function, contains a logic bug that silently suppresses email change notifications, and is wide open to SQL injection.

🔴 Change Propagation — Single Method Changes for Four Unrelated Business Reasons

Symptom: update_profile performs profile field updates, email change notifications, loyalty points recalculation, and cache invalidation all in one method body. Source: Fowler — Refactoring — Divergent Change; Hunt & Thomas — The Pragmatic Programmer — Orthogonality Consequence: Any change to the loyalty formula risks breaking email notifications and vice versa. Every edit carries regression risk across four unrelated domains simultaneously. Remedy: Extract NotificationService, LoyaltyService, and UserCacheInvalidator. UserService.update_profile should orchestrate by calling each — it should hold no implementation logic itself.

🔴 Domain Model Distortion — Silent Logic Bug: Email Notification Never Fires

Symptom: user['email'] = email overwrites the old value before if user['email'] != email — the condition is always False. The notification is dead code. Source: McConnell — Code Complete — Ch. 17: Unusual Control Structures Consequence: Users are never notified when their email address changes. Silent data integrity failure — the system appears functional while violating a business rule. Remedy: Capture old_email = user['email'] before any mutation. Compare against old_email, not user['email'].

(+ 6 more findings including SQL injection, dependency disorder, magic numbers)

Architecture Audit with Dependency Graph

In Mode 2 (Architecture Audit), brooks-lint generates a Mermaid dependency graph at the top of the report. Modules are color-coded by severity: red = Critical findings, yellow = Warning, green = clean.

graph TD
    subgraph src/api
        AuthController
        UserController
    end
    subgraph src/domain
        UserService
        OrderService
    end
    subgraph src/infra
        Database
        EmailClient
    end

    AuthController --> UserService
    UserController --> UserService
    UserController --> OrderService
    OrderService --> UserService
    OrderService --> EmailClient
    UserService --> Database
    EmailClient -.->|circular| OrderService

    classDef critical fill:#ff6b6b,stroke:#c92a2a,color:#fff
    classDef warning fill:#ffd43b,stroke:#e67700
    classDef clean fill:#51cf66,stroke:#2b8a3e,color:#fff

    class OrderService,EmailClient critical
    class AuthController warning
    class UserService,UserController,Database clean

The graph renders natively in GitHub, Notion, and other Markdown environments — no extra tools needed.

See More Examples

The Full Gallery has real brooks-lint output across Python, TypeScript, Go, and Java — including PR reviews, architecture audits with Mermaid dependency graphs, tech debt assessments, and test quality reviews.

New to the decay risks? The Decay Risk Field Guide explains all six — diagnostic question, signature symptoms, source books, and remedy for each.

Benchmark

Tested across 3 real-world scenarios (PR review, architecture audit, tech debt assessment):

The gap isn't what Claude can find — it's what it consistently finds, with traceable evidence and actionable remedies every time.

Reproducible benchmarks

The table above is illustrative. These numbers are deterministic and you can reproduce them locally:

Parser fidelity — SARIF export and the CI gates depend on parsing the model's Markdown report correctly. Against a frozen corpus of 30 real, model-generated reports spanning all six modes (evals/benchmark-corpus.json), each paired with an independently graded finding inventory (a separate model pass, spot-checked by hand), the shipped parser scores — run npm run benchmark:

Because the parser is deterministic and the corpus is frozen, npm run benchmark gives everyone the same result, and npm test guards it as a regression. The corpus deliberately includes 9 false-positive / tradeoff reports (e.g. a ports-and-adapters design that looks like a dependency cycle) that must stay clean.

Scoring determinism — for a fixed finding set (2 Critical / 3 Warning / 1 Suggestion), the strictness presets produce exactly the scores their common.md table predicts: strict 34, balanced 54, legacy-friendly 74 — and only legacy-friendly leads with the top-three fixes.

Model quality — whether the model finds the right risks on real code is measured by the 57-scenario eval suite (evals/evals.json): npm run evals (structural) and npm run evals:live (live, needs ANTHROPIC_API_KEY).

Scope & honesty: the parser numbers are deterministic and exactly reproducible. The strictness and eval-suite figures are single-run live measurements against the model and vary slightly run to run. The parser benchmark measures report-parsing fidelity (does the tooling read every finding the report states?), not whether a given finding is "correct." The severity-count match is the fully independent signal; risk-code agreement also reflects the shared canonical name→code legend.

How It Compares

~ = occasionally / inconsistently

brooks-lint doesn't replace your linter. It catches what linters can't: architectural drift, knowledge silos, and domain model distortion — the problems that slow teams down for months before anyone notices.

Installation

Claude Code (recommended)

/plugin marketplace add hyhmrright/brooks-lint
/plugin install brooks-lint@brooks-lint-marketplace

Short-form commands (/brooks-review) are auto-installed on first session start — or run bash hooks/session-start yourself. To skip the marketplace: mkdir -p ~/.claude/skills/brooks-lint && cp -r skills/* ~/.claude/skills/brooks-lint/.

Gemini CLI · Codex CLI

/extensions install https://github.com/hyhmrright/brooks-lint   # Gemini CLI

Install the brooks-lint skill from hyhmrright/brooks-lint       # ask inside a Codex session

Or use the installer below: ./scripts/install.sh gemini / ./scripts/install.sh codex.

Every other platform — OpenCode · Cursor · Windsurf · Antigravity · pi · Copilot · Kiro · Factory Droid

brooks-lint ships as standard Agent Skills. Any agent that loads Agent Skills runs all six modes with no conversion — one command installs them:

# pick your platform; --project installs into the current repo instead of your global config
curl -fsSL https://raw.githubusercontent.com/hyhmrright/brooks-lint/main/scripts/install.sh | bash -s -- <platform>
#   <platform> = opencode · cursor · windsurf · antigravity · pi · kiro · copilot · droid · gemini · codex · agents

The installer copies the skills flat into the right folder, so the shared framework (../_shared/) always resolves — you can't get the layout wrong. Then just ask ("review this PR", "audit the architecture") and the matching skill auto-triggers from its description.

Kiro and Factory Droid also auto-register /brooks-review. New to skills, or using an agent not listed? See docs/getting-started.md.

🧪 Verification status. Claude Code, Gemini CLI, and Codex CLI are maintainer-verified. The eight platforms above are documented from each tool's official skill spec and verified at the file-layout level (the installer is tested), but not yet run end-to-end by the maintainer on every platform. Tried one — working or broken? Open an issue with the platform, version, and what you saw. Another Agent-Skills agent? It almost certainly works the same way — tell us and we'll add it.

Slash Commands

Syntax by platform. Claude Code also accepts the namespaced form /brooks-lint:brooks-review — short forms are auto-installed on first session start by the session-start hook. Codex CLI uses $brooks-review. Gemini CLI uses the table as written. OpenCode, Cursor, Antigravity, and pi invoke Agent Skills from each skill's description, so just ask ("review this PR", "where's our worst tech debt?"); for explicit invocation use the platform's own syntax (pi registers each skill as /skill:brooks-review). On every platform the skills also trigger automatically when you discuss code quality, architecture, or test health.

PR reviews include a lightweight Step 7 Quick Test Check automatically (skipped for docs-only diffs). For a full test audit, run /brooks-test; for a deep dive on any single dimension, use that dimension's own skill rather than /brooks-health.

Configuration

Place a .brooks-lint.yaml in your project root to customize review behavior:

version: 1

strictness: balanced   # strict | balanced (default) | legacy-friendly — softer scoring for legacy code

disable:
  - T5   # skip coverage metrics check — we don't enforce coverage

severity:
  R1: suggestion   # downgrade Cognitive Overload findings for this domain

ignore:
  - "**/*.generated.*"
  - "**/vendor/**"

# custom_risks:   # define project-specific Cx codes — see skills/_shared/custom-risks-guide.md
# suppress:       # downgrade specific findings by risk + path (e.g. accepted legacy debt)

Copy .brooks-lint.example.yaml as a starting point. All settings are optional — omit the file entirely for default behavior.

Why These Books, Why Now?

"The complexity of software is an essential property, not an accidental one." — Frederick Brooks

AI can help you write code faster, but it can't tell you whether you're building a cathedral or a tar pit — and the decay risks these authors identified only get sharper as generation gets cheaper. Adding an AI assistant doesn't fix cognitive overload or domain model distortion; generating more code increases change propagation and knowledge duplication; moving faster makes accidental complexity and dependency disorder more dangerous.

Project Structure

Every skill is one SKILL.md (trigger + process skeleton) plus its own guide:

brooks-lint/
├── .claude-plugin/ · .codex-plugin/  # plugin metadata per platform
├── skills/
│   ├── _shared/          # common.md (Iron Law, config, report template, Health Score)
│   │                     # source-coverage.md · decay-risks.md (R1–R6)
│   │                     # test-decay-risks.md (T1–T6) · remedy-guide.md · custom-risks-guide.md
│   ├── brooks-review/    # Mode 1: PR Review          → pr-review-guide.md
│   ├── brooks-audit/     # Mode 2: Architecture Audit → architecture-guide.md, onboarding-guide.md
│   ├── brooks-debt/      # Mode 3: Tech Debt          → debt-guide.md
│   ├── brooks-test/      # Mode 4: Test Quality       → test-guide.md
│   ├── brooks-health/    # Mode 5: Health Dashboard   → health-guide.md
│   └── brooks-sweep/     # Mode 6: Full Sweep         → sweep-guide.md
├── hooks/                # SessionStart hook
├── commands/             # short-form command wrappers (auto-installed by the hook)
├── evals/                # 57-scenario eval suite + frozen parser-fidelity corpus
└── assets/               # logo, banner, demo

CI/CD Integration

Automate brooks-lint on every PR using the GitHub Action:

# .github/workflows/brooks-lint.yml
name: Brooks-Lint PR Review
on:
  pull_request:
    types: [opened, synchronize, reopened]

jobs:
  brooks-lint:
    runs-on: ubuntu-latest
    permissions:
      pull-requests: write
    steps:
      - uses: actions/checkout@v4
        with:
          fetch-depth: 0
      - uses: hyhmrright/brooks-lint/.github/actions/[email protected]
        with:
          mode: review
          anthropic-api-key: ${{ secrets.ANTHROPIC_API_KEY }}
          fail-below: 70

See docs/github-action-example.yml for the full template.

The action posts the review as a PR comment and optionally fails the check if the Health Score drops below a threshold. If .brooks-lint-history.json is committed to your repo, the comment also includes a trend delta (e.g., "85 → 82 (−3) over last 3 runs").

Quality gates and Code Scanning. Beyond fail-below, the action exposes:

        with:
          mode: review
          anthropic-api-key: ${{ secrets.ANTHROPIC_API_KEY }}
          fail-on: critical            # fail on any Critical finding (none | warning | critical)
          fail-on-regression: true     # fail if the Health Score dropped vs the last run
          sarif-file: brooks-lint.sarif  # also upload findings to GitHub Code Scanning

fail-on-regression reads .brooks-lint-history.json, so commit that file to enforce "no new regressions". Setting sarif-file makes findings appear inline on the PR's Files changed tab and requires security-events: write permission on the job.

Cost: ~$0.05–0.15 per PR run depending on diff size and model. Recommend running on pull_request events only.

Roadmap

Current state (v1.4): 12-book foundation, 6 production decay risks (R1–R6) + 6 test decay risks (T1–T6), 6 skills, CI quality gates, SARIF output for GitHub Code Scanning, strictness presets, and a reproducible parser-fidelity benchmark.

Contributing

See CONTRIBUTING.md. The most valuable contributions right now are new eval test cases and improved decay-risk symptom patterns. Run /brooks-review on your own PR — we review contributions with the tool we're building.

License

MIT License — see LICENSE for details.

Acknowledgments

This project stands on the shoulders of twelve giants — see The Twelve Books above for the full list with editions. The decay risks encoded in this tool are our synthesis of their ideas, applied to modern code quality assessment.

Star History

⭐ If this tool helped you see your codebase differently, give it a star!