AI Check
Use when someone asks "does this sound AI?", "check if this is AI-written", "what gives...
Install
npx skills add harshaneel/humanize --skill ai-check -g -a claude-code -y- Repository
- harshaneel/humanize597
- Category
- Engineering
- Updated
- 1mo ago
- License
- MIT
- Context cost
- Mediumabout 3,000 tokens
- Runs scripts
- No, instructions only
What it does
Use when someone asks "does this sound AI?", "check if this is AI-written", "what gives this away as AI", "run ai-check on this", or "score this text". Also use when reviewing a draft for AI tells before publishing, or when a piece of text reads as suspiciously polished, generic, or pattern-y and the user wants a forensic breakdown of why.
AI-Check Skill
Forensic analysis of text for AI-generation signals. Grounded in the published detection literature (Wu et al. 2025, Mitchell et al. 2023, Kujur 2025, AAAI 2025 shared task).
The output is a structured report, not a vague judgment. Every fired signal cites evidence.
The nine signal categories
Score each category 0, 3:
- 0 = No signal detected (human-consistent)
- 1 = Weak signal (possible AI, could be human)
- 2 = Moderate signal (likely AI pattern)
- 3 = Strong signal (near-certain AI pattern)
Severity-to-score mapping (use for every category):
| Evidence in category | Score |
|---|---|
| No flagged instances | 0 |
| One weak instance, or vague unease without a specific quote | 1 |
| One moderate instance, or two or more weak instances | 2 |
| One strong instance, or two or more moderate instances, or four or more weak instances | 3 |
Double-counting policy: a single phrase can fire at most two distinct signals when the phrase is genuinely diagnostic for both. Example: "it is important to note that" is both Signal A (banned vocabulary) and Signal C (institutional hedge). Log it under both, but the same phrase cannot count as two separate weak instances inside the same category.
Total score cap: 9 categories × 3 = 27 maximum.
Signal A: Perplexity (word predictability)
Look for vocabulary that is maximally safe and expected, words that are technically correct but never the most precise or interesting choice a knowledgeable human would make.
