ai-hygiene-auditor

ai-hygiene-auditor is an agent for Claude Code from athola/claude-night-market. It costs 48 tokens per session (6,396 once invoked), scanned A, original, MIT.

An agent that checks code for quality problems commonly introduced by AI-assisted coding, such as copied patterns, hallucinated packages, and tests that cover only the happy path.

In plain words
What is it for?
Use it to review AI-generated code, check whether dependencies are real, assess refactoring needs, and identify weak test coverage.
Why use it?
Traditional code checks can miss code that works but is hard to understand or maintain. This audit looks for AI-specific warning signs and maintenance risks.

Agent for Claude Code

Written for Claude Code: effort in frontmatter. Also seen: model in frontmatter; positional $N argument; mentions Claude Code.

Part of the conserve plugin — 15 skills, 6 commands, 5 agents shipped together

Good fit Use it to review AI-generated code, check whether dependencies are real, assess refactoring needs, and identify weak test coverage.

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Install with agentmods
npx agentmods add agents/athola/claude-night-market/ai-hygiene-auditor
Install

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.

Clone the repo
git clone --depth 1 https://github.com/athola/claude-night-market

Made for: Claude Code.

Or install conserve, the plugin that ships this one along with the rest of its 15 skills, 6 commands, 5 agents.

Wrote 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.

agentmods badge for ai-hygiene-auditor

README.md
[![agentmods](https://agentmods.dev/badge/agents/athola/claude-night-market/ai-hygiene-auditor/github.svg)](https://agentmods.dev/agents/athola/claude-night-market/ai-hygiene-auditor)
Your own site
<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.

agentmods 80×15 button for ai-hygiene-auditor

Your own site · 80×15
<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>
Per session 48 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 6,396 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 8d ago against content hash 918cc1b18838, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

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.

plugins/conserve/agents/ai-hygiene-auditor.md · 759 lines

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

  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):

# 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

Read the full file on GitHub · 759 lines

Changes

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.

  1. 8d ago First seen · 759 lines · 48 tokens per session scan A 918cc1b18838

Subscribe to this mod's changes

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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