/ai-hygiene-audit

name: ai-hygiene-audit
description: Audit codebase for AI-generated code quality issues (vibe coding, Tab bloat, slop)
usage: /ai-hygiene-audit [--focus git|duplication|tests|docs] [--report FILE] [--threshold SCORE]

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-Completion Bloat)

• **Repeated blocks**: 5+ line duplicates across files
• **Suffix variants**: names that differ only by `_v2`, `_new` or

`_old`, outside test modules

• **Copy-paste patterns**: Same logic with minor variations
• **Competing implementations**: One function name with different

bodies in two or more files, listed in the same report

Detection uses built-in `detect_duplicates.py` script (no external dependencies):

python3 plugins/conserve/scripts/detect_duplicates.py . --min-lines 5
python3 plugins/conserve/scripts/detect_duplicates.py . --format json --threshold 15

Test Quality

• **Happy path only**: Tests without error/exception assertions
• **Test deficit**: <30% test-to-code ratio by lines
• **Trivial coverage**: Tests that verify nothing meaningful

Documentation Slop

• **Hedge word density**: "worth noting", "arguably", "to some extent"
• **Formulaic structure**: Generic patterns without depth
• **Surface insights**: Describes WHAT without explaining WHY

Code-Level AI Debt

• **Comment ratio**: >30% comment lines signals restating/narrating code
• **Log density**: >3.0 log calls per function signals debug leftovers
• **Guard density**: >2.0 null/undefined checks per function signals defensive overengineering
• **Generic naming**: `handle_data`, `process_item` in domain code where specific terms exist
• **Pass-through wrappers**: Functions that delegate without adding logic
• **Docstring bloat**: Multi-line docstrings on trivial 2-3 line functions

See the ai-hygiene-auditor agent for thresholds and false-positive exclusions.

Dependency Verification

• **Hallucinated packages**: Imports for non-existent modules
• **Slopsquatting risk**: Plausible-sounding fake packages

Example Output

=== AI Hygiene Audit ===
Score: 62/100 (MODERATE CONCERN)

FINDINGS:

[HIGH] Tab-Completion Bloat
  src/handlers/*.py: 4 near-identical classes
  Recommendation: Extract to shared base class
  Impact: ~2,400 duplicate tokens

[HIGH] Happy Path Tests
  tests/test_api.py: 0 error assertions in 847 lines
  Recommendation: Add pytest.raises tests

[MEDIUM] Refactoring Deficit
  2.3% of commits mention refactoring (target: >10%)
  Recommendation: Add refactoring to sprint goals

[LOW] Documentation Slop
  docs/api.md: 23 hedge phrases per 1000 words
  Recommendation: Rewrite with concrete specifics

NEXT STEPS:
  1. /unbloat --focus duplication
  2. Add error tests before new features
  3. Review massive commits for understanding gaps

CI Integration

# GitHub Actions example
- name: AI Hygiene Check
  run: |
    claude "/ai-hygiene-audit --threshold 60 --json" > hygiene.json
    if [ $(jq '.score' hygiene.json) -lt 60 ]; then
      echo "AI hygiene score below threshold"
      exit 1
    fi

Relationship to Other Commands

| Command | Focus | Use Case | |---------|-------|----------| | `/bloat-scan` | Dead/unused code | Find DELETE candidates | | `/ai-hygiene-audit` | AI-specific issues | Find REFACTOR candidates | | `/unbloat` | Remediation | Fix findings from both |

**Workflow:**

/bloat-scan --level 2              # Traditional bloat
/ai-hygiene-audit                  # AI-specific issues
/unbloat                           # Address both

See Also

• `ai-hygiene-auditor` agent - Implementation details
• `@module:ai-generated-bloat` - Detection patterns
• `imbue:scope-guard/anti-overengineering` - Agent psychosis warnings
• Knowledge corpus: `agent-psychosis-codebase-hygiene.md`