# AI Audit

> Detect and report AI fingerprints / slop in projects. Run a comprehensive audit looking for the recognizable tells of AI-generated content across three dimensions: COPY (em dashes, LLM prose tics, marketing buzzwords, aphoristic cadence), DESIGN (overused fonts, gradient text, glassmorphism, card-grid patterns, AI color palettes), and CODE (verbose AI-style comments, over-engineering, generic naming, hallucinated imports). Auto-detects the right mode from context — override with --mode copy|design|code|full. Always use this when the user says "audit", "ai slop", "ai fingerprints", "quality check", "review this page", "check for ai", "remove ai tells", or expresses concern that something looks/sounds like it was generated by AI.

- Skill: `ajeesh25353646/ai-audit` (Agent Skill, multi-file: 4 files)
- Install (CLI): `npx skillmds@latest add ajeesh25353646/ai-audit`
- Raw SKILL.md: https://api.skillmd.com/api/skills/ajeesh25353646/ai-audit/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Marketing & Growth
- Author: Ajeesh25353646 (https://skillmd.com/u/ajeesh25353646)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/ajeesh25353646/ai-audit

---


# AI Audit Skill

Audit any project or piece of content for recognizable AI fingerprints — the visual, copy, and code patterns that AI models
default to. The skill produces a structured report you can act on directly.

## How Mode Detection Works

The skill picks the right audit scope automatically, using this priority:

1. **User's explicit words** — "audit this landing page" → `design+copy`. "check this function" → `code`. "audit everything" → `full`.
2. **Files in context** — if the user attached / mentioned specific files, peek at their extensions and content to guess the mode.
3. **Project scan** — glance at the directory structure. `.py`/`.js`/`.ts` files suggest code. `.html`/`.css`/`.jsx` suggest design. `.md`/`.txt` suggest copy.
4. **Fallback** — if nothing is clear, run `full` (all three modes).

**Override**: Pass `--mode copy`, `--mode design`, `--mode code`, or `--mode full` to force a specific scope.

When the mode is ambiguous (e.g., user just says "audit this" with nothing else to go on), ask briefly rather than guessing wrong.

## Workflow

When invoked, follow these steps:

### 1. Determine Scope

Figure out the mode. If forced via `--mode`, use that. Otherwise, apply the priority chain above. If truly ambiguous after checking, ask.

### 2. Load Reference Rules

Based on the mode, read the relevant reference file(s):

| Mode | Reference file | What it covers |
|------|---------------|----------------|
| copy | `references/copy-fingerprints.md` | LLM prose tells, buzzwords, em dashes, cadence patterns |
| design | `references/design-fingerprints.md` | Visual anti-patterns, overused fonts, AI color palettes, layout patterns |
| code | `references/code-fingerprints.md` | AI comment patterns, over-engineering, generic naming, hallucinated imports |
| full | All three | Everything — this is the comprehensive audit |

Read the reference file(s) in full — each is designed to load on demand and contains the specific patterns to look for.

### 3. Scan the Target

**If files are specified** (user named a file, passed a URL, or a file is in their message):
- Read the file(s) or fetch the URL content
- Apply the relevant rule set(s) from the reference files
- For each rule, check the content systematically

**If no files are specified but the user wants to audit the project:**
- Scan the current directory for relevant files (by extension per mode)
- Pick the most likely targets (e.g., main pages, key components, entry points)
- Sample a representative set — don't read every file in a large project
- Apply the relevant rule set(s)

**If the user pasted content inline**, audit that content directly.

### 4. Generate the Report

Print the report to stdout AND write it to `ai-audit-report.md` in the current directory.
Optionally also write `ai-audit-report.json` for programmatic use.

Use the report template below. Every finding should include enough context for someone reading the report to understand *what* was found, *why* it's a fingerprint, and *how* to fix it.

### 5. Natural Next Steps

After the report lands in context and on disk, Claude or the user can naturally act on it — fix the em dashes, swap out the font, clean up the code — because the report lists actionable fixes alongside each finding. No special fix mode is needed; the report is the trigger.

## Report Template

```markdown
# AI Audit Report

**Target:** <file(s) or description>
**Mode:** copy | design | code | full
**Date:** <date>

## Summary

- **Total findings:** N
- **AI Slop Score:** 0–4 (0 = heavy AI fingerprints, 4 = no detectable tells)
- **By severity:** P0: X, P1: Y, P2: Z
- **By category:** copy: X, design: Y, code: Z

## Findings

### [P0|P1|P2] Finding Title
- **Location:** `file.ts:42`
- **Category:** copy/design/code
- **What:** The exact text or pattern found
- **Why it's a tell:** Brief explanation of why this reads as AI-generated
- **Fix:** Specific, actionable suggestion for what to change it to

...

## No Issues Found

If the audit finds no detectable AI fingerprints, report that plainly.
No fake findings. If it's clean, say so.
```

### Severity Guide

- **P0 — Definite slop**: Unambiguously an AI tell. Em dashes in bulk, `--mode=cod` gradient text, overused fonts, LLM cadence patterns. Should be fixed.
- **P1 — Likely slop**: Strong signal. Cream/beige palette, single font for everything, aphoristic copy. Worth fixing but may be intentional in context.
- **P2 — Advisory**: Weak signal. Something that *could* be AI-generated but might also be a deliberate choice. Flag it but don't insist.

### Scoring

The AI Slop Score follows Impeccable's convention:

| Score | Meaning |
|-------|---------|
| **4** | No detectable AI fingerprints — clean |
| **3** | Minor tells — a few patterns but not pervasive |
| **2** | Moderate — several clear signals |
| **1** | Heavy — obvious AI generation throughout |
| **0** | AI slop gallery — every page/section has multiple tells |

Score from 4 and subtract per finding: P0 = -1.5, P1 = -1, P2 = -0.5. Clamp to [0, 4].

## Important Constraints

- **Be honest about clean content.** The skill's credibility depends on not hallucinating findings. If content reads human-written, say so.
- **Be specific, not vague.** Don't say "this looks AI-generated" without pointing to a concrete pattern from the reference file. Every finding must have a detectable, nameable pattern behind it.
- **Don't over-audit dependencies.** Code audit should focus on the project's own code, not library code in `node_modules/`, `.venv/`, or similar.
- **Severity matters.** A single em dash in 2000 words is P2, not P0. Context and density are everything.