Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/athola/claude-night-marketWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/athola/claude-night-market/ai-hygiene-auditor)<a href="https://agentmods.dev/agents/athola/claude-night-market/ai-hygiene-auditor"><img src="https://agentmods.dev/badge/agents/athola/claude-night-market/ai-hygiene-auditor/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/athola/claude-night-market/ai-hygiene-auditor"><img src="https://agentmods.dev/badge/agents/athola/claude-night-market/ai-hygiene-auditor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00048 | $0.06396 |
| Opus 5 | $0.00024 | $0.03198 |
| Sonnet 5 | $0.00010 | $0.01279 |
| Haiku 4.5 | $0.00005 | $0.00640 |
Grade A, and why
ai-hygiene-auditor scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 759 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Hygiene Auditor Agent
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
- Detect AI Patterns: Identify vibe coding, Tab-completion bloat, slop
- Assess Understanding Risk: Flag code that may not be understood by maintainers
- Measure Refactoring Deficit: Compare addition vs refactoring ratios
- Verify Dependencies: Check for hallucinated packages
- 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):
| # | Tell | comment% | Notes |
|---|---|---|---|
| 1 | Boilerplate / tutorial-shaped code | 18.6% | #1 by wide margin; 90% precision |
| 2 | Hallucinated APIs / made-up methods | 11.2% | language-agnostic; bites at runtime |
| 3 | Over-commenting (every line narrated) | 8.5% | inflated; only 48% of tags confirmed |
| 4 | Over-engineering / needless abstraction | 7.8% | "KISS, YAGNI" in agent instructions fixes it |
| 5 | Emoji in code / comments / commits | 3.9% | highest precision of any cosmetic tell |
| 6 | Style mismatch (ignores codebase) | 3.5% | a 50-LoC PR becoming 2000-LoC because conventions ignored |
| 7 | try/except wrapping everything | 3.1% | errors swallowed silently |
| 10 | Generic placeholder names | 1.9% | process_data() that does 11 things; 100% precision |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 759 lines · 48 tokens per session scan A 918cc1b18838
ai-hygiene-auditor is an agent published in the GitHub repository athola/claude-night-market (337 stars, last pushed yesterday), licensed MIT. It adds 48 tokens to every session and 6,396 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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