codebase-audit

# Codebase Audit — Adaptive Deep Analysis A comprehensive codebase audit that adapts its agent configuration to the project's tech stack. Each agent uses opus for maximum thoroughness. Results are compiled into a unified report sorted by severity with a phased repair roadmap. ## Core Principles 1. **Opus only** — All audit agents MUST use `model="opus"`. This is non-negotiable. Smaller models miss subtle cross-file issues. 2. **Depth over breadth** — Fewer agents with broader scope and deeper analysis beats many shallow agents. Each agent should trace issues across file boundaries. 3. **Adaptive** — Agent count and focus areas vary by project type. Don't waste an agent on "frontend rendering" for a backend-only project. ## When to Use - User asks to audit/review/analyze an entire codebase - User wants to find hidden bugs, silent degradation, or design inconsistencies - User asks about technical debt, architecture health, or "what's broken" - Before a major refactor or after inheriting an unfamiliar codebase - Periodic health check (monthly/quarterly) ## Workflow ### Phase 0: Tech Stack Detection Detect the project's tech stack to determine the agent configuration: ``` Detection checklist: - package.json / tsconfig.json → TypeScript/JavaScript (React, Next.js, Vue, etc.) - pyproject.toml / requirements.txt / setup.py → Python (FastAPI, Django, Pydantic, etc.) - Cargo.toml → Rust (serde, axum, actix, etc.) - go.mod → Go (gin, echo, gorm, etc.) - Multiple stacks → Full-...