By hyhmrright
Run AI-powered code reviews grounded in twelve classic engineering books. Analyze PRs, architecture, tech debt, and test quality with severity labels and book citations, then auto-fix safe issues or generate a health dashboard.
Run a Brooks-Lint architecture audit
Run a Brooks-Lint tech debt assessment
Run a Brooks-Lint codebase health dashboard across all four dimensions
Run a Brooks-Lint PR code review
Run all Brooks-Lint analyses in one pass, auto-fix safe findings, confirm risky ones
Architecture audit that maps module dependencies, checks layering integrity, and flags structural decay across a codebase, drawing on twelve classic engineering books. Triggers when: user asks to audit architecture, review folder/module structure, check for circular imports, understand how the codebase is organized, or asks "does this follow clean architecture?", "why does everything depend on everything?", "are our layers correct?", "where should this code live?". Also triggers for onboarding requests: "explain this codebase to a new developer" or "give me a codebase tour" (use onboarding mode). Do NOT trigger for: PR-level code review (use brooks-review) or line-level refactoring questions — this skill analyzes structural/module-level concerns, not individual functions.
Tech debt assessment that identifies, classifies, and prioritizes maintainability problems — helping teams build a refactoring roadmap — drawing on twelve classic engineering books. Triggers when: user asks about tech debt, refactoring priorities, what to clean up first, or asks "why is this so hard to change?", "where's the most painful part?", "what should we fix first?", "how do I justify refactoring to management?", "why is our velocity dropping?". Do NOT trigger for: server health checks, HTTP /health endpoints, Kubernetes probes, database health, or application uptime — "health" in those contexts is infrastructure, not code quality. Also not for single-function refactoring questions.
Combined codebase health dashboard that scores a project across all four quality dimensions — PR quality, architecture, tech debt, and test quality — in a single pass, drawing on twelve classic engineering books. Triggers when: user wants an overall quality assessment, asks "how healthy is this codebase?", "run all the checks", "give me a big-picture quality report", "I need a health score before the release", "what's the overall state of our code?", or wants to onboard a new team with a quality overview. Do NOT trigger for: server health checks, HTTP health endpoints, Kubernetes liveness/readiness probes, database health, or application uptime. Also do not trigger when the user specifically requests only one dimension — use the corresponding focused skill instead (brooks-review / brooks-audit / brooks-debt / brooks-test).
PR code review that surfaces decay risks, design smells, and maintainability issues with concrete Symptom → Source → Consequence → Remedy findings, drawing on twelve classic engineering books. Triggers when: user asks to review code, check a PR, shares a diff or pastes code asking "does this look right?" / "any issues here?" / "ready to merge?", or asks for feedback on a function, class, or file. Also triggers when user mentions: code smells / refactoring / clean architecture / DDD / domain-driven design / SOLID principles / Hyrum's Law / deep modules / tactical programming / conceptual integrity / Brooks's Law / Mythical Man-Month / second system effect. Do NOT trigger for: questions about how to write code from scratch, language syntax questions, or framework/tool questions where no existing code is shared.
Full-sweep mode: runs a unified analysis across all quality dimensions — code decay, architecture, tech debt, and test quality — then applies fixes directly to the codebase. Safe changes are auto-applied; risky changes are confirmed before execution. Drawing on twelve classic engineering books. Triggers when: user wants to "fix everything", "sweep the codebase", "auto-fix all issues", "run all checks and fix them", "clean up the whole project", or asks for a single command that both diagnoses and remediates quality problems. Do NOT trigger for: read-only audits or health reports where the user only wants findings without code changes; single-dimension reviews (use the focused skill instead: brooks-review / brooks-audit / brooks-debt / brooks-test); server health checks, HTTP /health endpoints, Kubernetes probes, or application uptime.
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AI code reviews grounded in twelve classic engineering books.
Consistent. Traceable. Actionable.
English · 简体中文
The Six Decay Risks • What It Looks Like • Benchmark • Installation
"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.
| Book | Author | Contributes to |
|---|---|---|
| The Mythical Man-Month | Frederick Brooks | R2, R4, R5 |
| Code Complete | Steve McConnell | R1, R4 |
| Refactoring | Martin Fowler | R1, R2, R3, R4, R6 |
| Clean Architecture | Robert C. Martin | R2, R5 |
| The Pragmatic Programmer | Hunt & Thomas | R2, R3, R4, R5, T2, T3 |
| Domain-Driven Design | Eric Evans | R1, R3, R6 |
| A Philosophy of Software Design | John Ousterhout | R1, R4 |
| Software Engineering at Google | Winters, Manshreck & Wright | R2, R5 |
| The Art of Unit Testing | Roy Osherove | T1, T2, T4, T5 |
| How Google Tests Software | James A. Whittaker, Jason Arbon & Jeff Carollo | T5, T6 |
| Working Effectively with Legacy Code | Michael Feathers | T4, T5, T6 |
| xUnit Test Patterns | Gerard Meszaros | T1, T2, T3, T4 |
brooks-lint evaluates your code across six production-code decay risks and six test-suite decay risks synthesized from twelve classic engineering books:
| Decay Risk | Diagnostic Question | Sources |
|---|---|---|
| 🧠 Cognitive Overload | How much mental effort to understand this? | Code Complete, Refactoring, DDD, Philosophy of SD |
| 🔗 Change Propagation | How many unrelated things break on one change? | Refactoring, Clean Architecture, Pragmatic, SE@Google |
| 📋 Knowledge Duplication | Is the same decision expressed in multiple places? | Pragmatic, Refactoring, DDD |
| 🌀 Accidental Complexity | Is the code more complex than the problem? | Refactoring, Code Complete, Brooks, Philosophy of SD |
| 🏗️ Dependency Disorder | Do dependencies flow in a consistent direction? | Clean Architecture, Brooks, Pragmatic, SE@Google |
| 🗺️ Domain Model Distortion | Does the code faithfully represent the domain? | DDD, Refactoring |
Philosophy of SD = A Philosophy of Software Design (Ousterhout) · SE@Google = Software Engineering at Google (Winters et al.)
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
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