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Claude Agents Collection

Specialized Claude AI agent configurations for software development workflows. Each agent follows SLON, KISS, and Occam's razor principles.

Core Principles

  • SLON - Simplicity, Lean solutions, One clear thing, No over-engineering
  • KISS - As simple as possible, but not simpler than necessary
  • Occam's Razor - Every abstraction must justify its existence
  • DRY - Extract shared logic, avoid repetition

Agents

Agent Model Purpose When to Use
architect Sonnet Systems architecture, design decisions Design, refactoring planning, architecture evaluation
prd-writer Opus LLM-optimized PRDs Formalizing requirements before development
worker Opus Production-ready TS/JS code Implementing features, executing plans
test-writer-worker Sonnet Unit/integration tests Writing tests for components, sagas, validators
refactor Opus Code modularization Files >500 lines, breaking monoliths
test-auditor Opus Test coverage audit Verifying test quality and coverage
linus-code-reviewer Opus Brutally honest code review PR reviews, validating AI-generated code

Scripts

Utility scripts for test and type checking workflows.

Script Purpose When to Use
analyzeCoverage.js Parses lcov.info, outputs coverage stats by directory After running tests with coverage to find weak spots
extract-tests.js Runs tests with progress bar, ETA, failure extraction Need detailed failure reports (test-results.failed.txt)
tscFilesRunner.js TypeScript check for specific files Manual TS check on arbitrary files
tscParser.js TypeScript check for git staged files Pre-commit TS validation

Usage:

# Coverage analysis (requires coverage/lcov.info)
node scripts/analyzeCoverage.js

# Run tests with progress tracking
node scripts/extract-tests.js

# Check specific files
node scripts/tscFilesRunner.js --files "src/a.ts,src/b.tsx"

# Check staged files
node scripts/tscParser.js

Workflow

All tasks follow the same flow regardless of complexity:

1. Enter Planning Mode

Use Claude's planning mode or prompt: "Create a detailed step-by-step plan, analyzing the information below"

2. Write Your Prompt

  • Reference files that may be related to the issue (without assumptions - avoid biasing the AI)
  • Specify info gathering: use multiple @explore subagents to get info, search files and research
  • Specify execution: use multiple parallel @worker to implement the plan
  • End with: "analyze the information you find and provide at least 3 possible causes"

3. For Complex Bugs (optional)

Add reverse analysis: "Conduct reverse analysis from the component where X is used to its source code"

4. Refine the Plan

Discuss with AI, iterate until the plan is ideal. Challenge assumptions, debate approaches if needed.

5. Execute & Monitor

Accept the plan and monitor execution. Correct in real-time or interrupt if it goes off track.

6. Context Management

If context is low (15-20% Claude / 50% Codex remaining):

  1. Ask AI to save the complete plan to a file
  2. Restart session with clean context
  3. Attach the plan and prompt: Get necessary information with multiple @explore subagents in parallel

This allows targeted context gathering since the AI knows exactly what to look for.

License

GNU General Public License v3.0. See LICENSE for details.

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A small collection of Claude agents with my workflow

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