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Testing48k · #2447

brooks-lint

Helps your agent review code, finding design flaws and architectural risks. It draws on classic software engineering books.

What it does

This program analyzes your programming code using lessons from 12 classic software engineering books. It looks for design issues, like repeated code or complicated dependencies, that common tools might miss.

When to use it

  • ✓When you want feedback on your code's structure beyond the basics.
  • ✓Before making major code changes to find weak spots.
  • ✓When code works but feels confusing or poorly organized.

Example of what to ask your AI

“Review this code and tell me if it follows good design practices.”

Want more? Open the sections below.

How to use

The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.

Skill link

https://github.com/sickn33/agentic-awesome-skills/blob/main/plugins/agentic-awesome-skills-claude/skills/brooks-lint/SKILL.md

Ready-to-paste message

Use this skill to help me: https://github.com/sickn33/agentic-awesome-skills/blob/main/plugins/agentic-awesome-skills-claude/skills/brooks-lint/SKILL.md Read the SKILL.md file at that link and follow its instructions for my task.
How it works

It compares your code against principles from famous programming books like 'Clean Code' and 'The Pragmatic Programmer'. It identifies bad patterns and suggests architectural improvements.

Installation

Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.

In the terminal (Claude Code, Codex, Cursor), in your project folder:

npx skills add sickn33/agentic-awesome-skills --skill brooks-lint

Or download just the file:

curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/brooks-lint/SKILL.md -o SKILL.md
Technical content (original SKILL.md)

Original text by the author, straight from GitHub.

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

…

FAQ

Why is it called 'Brooks Lint'?

It's named after Fred Brooks, author of 'The Mythical Man-Month', who believed the hardest bugs are conceptual, not typos.

What kinds of problems does it find?

It finds issues like duplicated code (DRY), poor naming, tight coupling between code parts, and architectural flaws that could cause future problems.

Does it replace a human reviewer?

No, it's a helpful tool. It's good for a second opinion based on established principles, but critical decisions should still be made by humans.

Details

Original description by the author

AI code reviewer grounded in classic software engineering books for catching design smells, coupling issues, and architectural risks.

repo: sickn33/agentic-awesome-skills

Popularity

GitHub stars
48k
Ranking position
#2447
License
MIT
Updated
10/15/2026

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