ai-checklisted

# 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 hum