What It Does
Answers two questions — how much does this read as machine-generated? and does it carry an embedded mark? — with four independent measures:
- Heuristic audit. Flags known AI tells (inflated vocabulary, the "not X, it's Y" tic, recycled transitions, uniform rhythm) against a severity-tiered pattern catalog. Free, instant, and it explains why each flag fired.
- Statistical signals. A deterministic pass (
LIFEOS/TOOLS/StatSignals.ts) measuring the keyless distributional tells the research literature rates real: n-gram entropy, type-token ratio, repetition structure — plus the weak-alone folklore tier (burstiness, paragraph uniformity, function-word stylometry), each labeled with its reliability. Features, never verdicts (arXiv:2310.15264: paraphrase degrades every keyless statistic). Free, no key. - Empirical score. Runs the text through the Pangram detection model and returns a real probability — AI% / AI-assisted% / human% — plus per-segment counts. Costs money, needs an API key, and doesn't care what your word list says.
- Watermark scan. A keyless, deterministic pass for character-level covert channels — invisible chars, variation-selector/Tags-block steganography, homoglyphs, bidi, odd whitespace. Catches embedded marks that live in the bytes; by design it cannot read sampling-time statistical watermarks (SynthID, Kirchenbauer, Anthropic's announced mark), which are key-gated. Free, no key.
The measures disagree often, and that disagreement is the useful part. Text can clear every pattern on the list and still score 100% AI, which tells you the tells are structural, not lexical — and a watermark hit is bytes-level proof regardless of what the other two say.
The Problem
"Does this sound like AI?" gets answered by vibes, and vibes are wrong in both directions. Heuristic word-lists flag legitimate writing and miss AI text that avoided the obvious words. Detector scores look authoritative but saturate — Pangram will confidently call a short human paragraph 100% AI. Neither measure alone is trustworthy, and a raw number with no baseline is close to meaningless.
This skill runs both, and anchors the empirical score against known-human writing so the number has something to be read against.
Setup — Pangram API key (required for scoring)
The heuristic audit works with no setup. The empirical score needs a key.
- Create an account or log in at pangram.com, open the API tab, and generate a key.
- Add prepaid credits (from $5, or enable auto-refill). Realtime checks bill about $0.05 per 1,000 words.
- Put the key in
~/.claude/.env:PANGRAM_API_KEY=your-key-here - Verify:
bun ~/.claude/LIFEOS/TOOLS/PangramScore.ts --file <a-file-you-wrote.md>
Full setup, alternatives, and troubleshooting (402/429 handling, endpoint override, key precedence): Setup.md.
Workflow Routing
| Workflow | Trigger | File |
|---|---|---|
| Detect | "scan for AI tells", "flag AI patterns", "does this sound like AI", "audit this for AI-isms", "statistical signals", "burstiness", "entropy" — heuristic + deterministic statistical pass, no key needed | Workflows/Detect.md |
| Score | "score this for AI", "is this AI generated", "AI detection score", "pangram", "compare these drafts" — empirical, needs key | Workflows/Score.md |
| Watermark | "is this watermarked", "detect a watermark", "scan for hidden/invisible characters", "steganography", "zero-width chars" — keyless byte-level signature scan, no key needed | Workflows/Watermark.md |
Asked simply "is this AI?" with a key configured, run both and report them side by side — the heuristic explains, the score measures. "Is this watermarked?" routes to Watermark, which answers a different question: whether a covert channel is embedded in the bytes, not whether the prose reads as AI.
Gotchas
- Short samples are unreliable. Detectors are weakest under ~5 sentences. Pangram leans toward decisive 100/0 calls and will flag a short human paragraph as 100% AI. Verified in testing: a plain-voice human paragraph and deliberate AI slop both scored 100% at roughly four sentences each. Score passages of a few hundred words or don't bother.
- An absolute score without a baseline says little. Score known-human writing in the same batch. If the human baseline also maxes out, the detector is saturating on the genre and length, not on the text. The A-vs-B comparison is the trustworthy part.
- It measures detectability, not quality. A low AI% means "reads human," not "reads well."
- One detector is not ground truth. Pangram is among the strongest available and still has real false-positive rates. Report it as a strong signal, never a verdict — and never accuse a person of AI authorship on one score.
- Every call bills and polls. The API is async (submit, then poll to
STAGE_SUCCESS). Don't loop it on trivial snippets; batch comparisons run sequentially, one call each. - HTTP 402 means out of credits, not a bad key. 429 means rate limited — realtime checks cap at 5 QPS. Neither is an auth failure; don't rotate the key over them.
- Never degrade writing to beat a detector. Injected typos, broken sentences, and "humanizer" laundering damage the prose and don't fix the underlying problem. If text must read human, the fix is a human in the loop.
Examples
- "Scan this post for AI tells, don't change it" → Detect: tiered P0/P1/P2 flag report, each marked clear-problem vs judgment-call, no edits.
- "Is this AI generated?" → Score: single Pangram run, headline verdict plus AI/AI-assisted/human percentages, with the length caveat stated if the sample is short.
- "Which of these three drafts reads most human?" → Score in batch-compare mode: one call per draft, ranked table, relative comparison foregrounded over absolute numbers.
- "Did my rewrite actually help?" → Score before and after, with two known-human passages scored in the same batch as calibration.
Execution Log
echo '{"ts":"'$(date -u +%Y-%m-%dT%H:%M:%SZ)'","skill":"DetectAI","workflow":"WORKFLOW_USED","input":"8_WORD_SUMMARY","status":"ok|error","duration_s":SECONDS}' >> ~/.claude/LIFEOS/MEMORY/SKILLS/execution.jsonl