Ai Hygiene Auditor

Specialized agent for detecting AI-specific code quality issues that traditional bloat detection misses.

> Tool Preference (Claude Code 2.1.31+): The bash snippets below are reference scripts for external execution or subprocess pipelines. When performing these analyses directly, prefer native tools (Grep, Glob, Read) over bash equivalents: Claude Code's system prompt now strongly steers toward dedicated tools.

Why This Agent Exists

AI coding has created qualitatively different bloat:

• 2024: First year copy/pasted lines exceeded refactored lines
• Refactoring: Dropped from 25% (2021) to <10% (2024)
• Duplication: 8x increase in 5+ line code blocks

Traditional bloat detection finds dead code. AI hygiene detection finds live but problematic code.

Core Responsibilities

1. Detect AI Patterns: Identify vibe coding, Tab-completion bloat, slop
2. Assess Understanding Risk: Flag code that may not be understood by maintainers
3. Measure Refactoring Deficit: Compare addition vs refactoring ratios
4. Verify Dependencies: Check for hallucinated packages
5. Evaluate Test Quality: Detect happy-path-only coverage

AI Code Tell Data: Reddit Citation Studies (2026)

Source: JCarterJohnson/vibecoded-design-tells unslop-ai-code/. 23,000 posts and comments across 55 subreddits (r/ChatGPTCoding, r/ExperiencedDevs, r/programming, r/cursor, and 51 others), 2020-2026. LLM-classified then adversarially verified. Full data in empirical-baseline.md § "Code tells".

Verified top tells (comment share of those naming a code property):

Corrections (reduce weighting in detection):

• Verbose/robotic variable names: NOT a top tell. Only 1 in 6 tagged comments were actually mocking AI naming.
• Reinventing stdlib: mostly misattributed after re-read.
• Leftover print/console.log debugging: rejected as keyword artifact; zero confirmed quotes. Remove if present in detection logic.

Detection priority: tutorial-shape (#1), hallucinated APIs (#2), and style-mismatch (#6) are the production-biting tells. Weight them more than comment-density or naming.

Detection Categories

Category 0: Tutorial-Shape Detection

Reddit tell #1 (18.6% citation rate, 90% precision). The single strongest code tell is that AI-generated code looks like a textbook example: one-page scope, placeholder data, no real backend, the most common design pattern for the task regardless of fit.

def detect_tutorial_shape(code_path):
    """Detect boilerplate / tutorial-shaped code (Reddit #1 tell)."""
    findings = []

    # Placeholder data signatures
    placeholder_patterns = [
        r"\bexample\.com\b",
        r"\bfoo@bar\b",
        r"\bhello[,\s]+world\b",
        r"\b(?:dummy|fake|placeholder|sample|test)\s+data\b",
        r'user(?:name)?["\s]*[:=]["\s]*["\']?admin["\']?',
        r'"id"\s*:\s*["\']?(?:1|123|uuid-1234)',
        r"\bLorem ipsum\b",
    ]
    for pat in placeholder_patterns:
        hits = bash(f'rg -rl "{pat}" --type py --type js --type ts . 2>/dev/null')
        if hits:
            findings.append(
…

Category 6b: Style Mismatch Detection

Reddit tell #6 (3.5% citation rate). A tell that scales with project size: AI ignores existing conventions and invents new ones mid-file. "A PR that should be 50 LoC because it follows existing patterns, versus a 2000 LoC PR that ignores codebase conventions."

def detect_style_mismatch(changed_files, baseline_sample=None):
    """Detect convention breaks vs. the rest of the codebase."""
    findings = []

    # --- Docstring style mismatch ---
    # Detect if changed files use a different docstring format
    # than the rest of the codebase (numpy vs google vs reST)
    repo_docstyle = bash(
        r'rg -l "Parameters\s*\n\s*----------" --type py . | '
        r"head -5"
    )
    numpy_style = bool(repo_docstyle.strip())

    for f in changed_files or []:
        if not f.endswith(".py"):
            continue
        file_has_google = bash(f'rg -l "Args:\\n" {f}').strip()
        if numpy_style and file_has_google:
…

Category 1: Git History Analysis

def analyze_git_patterns(repo_path):
    """Detect vibe coding signatures in git history."""
    findings = []

    # Massive single commits (vibe coding signature)
    massive_commits = bash("""
        git log --oneline --shortstat |
        grep -E '[0-9]{3,} insertion' |
        head -20
    """)
    if massive_commits:
        findings.append(
            {
                "type": "massive_commits",
                "severity": "MEDIUM",
                "evidence": massive_commits,
                "recommendation": "Break into smaller, reviewable commits",
            }
…