Brooks Lint

Overview

Brooks Lint is a Claude Code skill that reviews your code through the lens of 12 classic software engineering books. Instead of checking style rules, it asks: "What would the authors of The Pragmatic Programmer, Clean Code, and Designing Data-Intensive Applications say about this code?"

It synthesizes the principles from landmark engineering books into actionable, structured feedback — catching design smells, tight coupling, missing abstractions, and architectural risks that linters and AI tools typically miss.

Named after Fred Brooks, author of The Mythical Man-Month — because the hardest bugs are conceptual, not syntactic.

The 12 Books

Book	Key Principles Applied
The Pragmatic Programmer	DRY, orthogonality, tracer bullets
Clean Code	Naming, function size, comment clarity
The Mythical Man-Month	Conceptual integrity, second-system effect
Designing Data-Intensive Applications	Data consistency, fault tolerance, scalability
A Philosophy of Software Design	Deep modules, information hiding, complexity
Refactoring	Code smells, extract method, encapsulation
Working Effectively with Legacy Code	Seams, characterization tests, dependency breaking
Domain-Driven Design	Ubiquitous language, bounded contexts, aggregates
Release It!	Stability patterns, timeouts, bulkheads, circuit breakers
Structure and Interpretation of Computer Programs	Abstraction, recursion, metalinguistic abstraction
The Art of UNIX Programming	Modularity, composability, rule of least surprise
Extreme Programming Explained	YAGNI, simple design, collective ownership

When to Use This Skill

• Use when you want architectural feedback beyond what linters provide
• Use before major refactors to identify structural debt
• Use when reviewing code that "works but feels wrong"
• Use when onboarding to a codebase to quickly map risk areas
• Use for design reviews before starting a new module or service

How It Works

Brooks Lint applies each book's core principles as a review lens:

1. Smell detection: Flags violations of DRY, SRP, Law of Demeter, etc.
2. Coupling analysis: Identifies tight dependencies and missing abstraction layers
3. Naming critique: Applies Clean Code naming rules to variables, methods, classes
4. Architecture review: Checks for DDIA-style data consistency and fault tolerance gaps
5. Stability patterns: Flags missing timeouts, retries, and circuit breakers (Release It!)
6. Complexity scoring: Applies APOSD complexity metrics to identify over-engineered sections

Installation

# Install via Claude Code plugin marketplace
# Search: "brooks-lint" in Claude Code > Extensions

# Or install via NPX (Antigravity)
npx agentic-awesome-skills --claude
# Then invoke: @brooks-lint

Examples

Example 1: Review a Service Class

@brooks-lint review src/services/PaymentService.ts

Brooks Lint output:

[Pragmatic Programmer] DRY violation: payment validation logic duplicated in 3 places
[Clean Code] Method processPayment() does 4 things — violates Single Responsibility
[Release It!] No timeout on external payment gateway call — risk of cascade failure
[DDIA] No idempotency key — retry on network error will double-charge
[APOSD] PaymentService knows too much about UserRepository — high coupling

Example 2: Full Codebase Architecture Review

@brooks-lint analyze the overall architecture of this codebase

Example 3: Pre-Refactor Review

@brooks-lint what are the biggest design smells in this module before I refactor it?

Review Categories

Category	Books Applied	What It Catches
DRY / Duplication	PP, Refactoring	Copy-paste code, shared logic not extracted
Naming	Clean Code, DDD	Unclear names, domain language violations
Coupling	APOSD, PP	Tight dependencies, missing interfaces
Stability	Release It!	Missing timeouts, no retry logic, no circuit breakers
Data Integrity	DDIA	Race conditions, non-idempotent operations
Complexity	APOSD, SICP	Over-engineering, unnecessary abstraction
Legacy Debt	WELC	Hard-to-test code, missing seams
Domain Clarity	DDD, XP	Anemic models, missing bounded contexts

Best Practices

• Run @brooks-lint after writing new service layers or data pipelines
• Combine with @logic-lens for full coverage: logic bugs + design smells
• Use @brooks-lint analyze architecture weekly on growing codebases
• Focus on CRITICAL and HIGH findings first — LOW findings are style suggestions

Related Skills

• @logic-lens — Complementary: catches logic bugs; brooks-lint catches design issues
• @security-auditor — Specialized security-only deep scan
• @lint-and-validate — Style/syntax linting to run alongside design review

Additional Resources

• GitHub Repository
• Dev.to Article: I Synthesized 12 Classic Engineering Books into an AI Code Reviewer
• Related skill: logic-lens

Limitations

Use this skill only when the task clearly matches the scope described above (design review and architectural analysis). Brooks Lint applies AI-powered analysis grounded in established engineering principles. It should complement — not replace — human design review for production-critical decisions. Results reflect the principles of the 12 source books and may not apply to all architectural styles or domains.