AI Hygiene Audit Command

Detect AI-specific code quality issues that traditional bloat detection misses.

When To Use

Use this command when you need to:

• Suspected AI-generated code quality issues
• Before major releases to check for hidden debt
• Reviewing PRs with suspected AI generation
• After rapid AI-assisted development sprints

When NOT To Use

• Quick fixes that don't need structured workflow
• Already know the specific issue - fix it directly

Why This Exists

AI coding creates different problems than human coding:

• 2024: First year copy > refactor in git history (GitClear)
• Tab-completion bloat: Similar code repeated instead of abstracted
• Happy path bias: Tests verify success, miss failures
• Slop: Documentation that sounds right but lacks depth

Usage

# Full AI hygiene audit
/ai-hygiene-audit

# Focus on specific area
/ai-hygiene-audit --focus git          # Git history patterns
/ai-hygiene-audit --focus duplication  # Tab-completion bloat
/ai-hygiene-audit --focus tests        # Happy-path-only detection
/ai-hygiene-audit --focus docs         # Documentation slop
/ai-hygiene-audit --focus code-debt    # Code-level AI debt signals

# Generate report file
/ai-hygiene-audit --report ai-hygiene-report.md

# Set pass/fail threshold (0-100)
/ai-hygiene-audit --threshold 70

Options

Option	Description	Default
--focus <area>	Limit to: git, duplication, tests, docs, deps, code-debt	all
--report <file>	Save detailed report to file	stdout
--threshold <score>	Fail if hygiene score below threshold	none
--json	Output structured JSON for CI integration	false

What It Detects

Git History Patterns

• Massive single commits: 500+ line additions (vibe coding signature)
• Refactoring deficit: <5% of commits involve refactoring
• Churn spikes: Code revised within 2 weeks of creation

Duplication (Tab-Comp