authenticity-check

name	authenticity-check
description	Score how authentically a piece of text reads as the work of a real human author, and flag the specific spans that read as AI-generated, AI-templated, or generically derivative. This is a diagnostic: it returns an authenticity band, a 0-100 score, and span-level flags with reasons. It never rewrites, edits, or "fixes" the prose; rewriting is the humanizer skill's job, and the split is deliberate. Use this whenever a user wants to know if text is authentic, whether a draft reads like AI or like a bot, how human a passage sounds, which parts sound machine-written, or whether their own draft still sounds like them. Reach for it on evaluative cues like "is this AI," "does this read like a person," "rate the AI-ness of this," "score how human this is," "audit this for AI tells," or "check if this still sounds like my voice," even when the user does not name this skill. Do not trigger this skill on transformative requests (humanize, de-slop, de-AI, rewrite, fix, make it sound like X); those belong to the separate humanizer skill.
allowed-tools	Read, Glob, Grep
compatibility	claude-code, cursor, codex, antigravity, gemini-cli, pi-coder, opencode, copilot, windsurf, cline, continue, zed, aider
metadata	{"version":"1.1.1"}

Authenticity Check

Read a piece of text and report how authentically it reads as the work of a person, then point at the exact spans that do not. The deliverable is a judgment plus evidence: an authenticity band, a 0-100 score, and a list of flagged spans with the reason each one reads as AI-generated, AI-templated, or generically derivative. The goal is an honest read a careful human editor would agree with, not a number tuned to beat any particular detector. This skill names and targets none.

This is the evaluative half of a pair. The other half, the humanizer skill, is a separate repository that rewrites prose. This skill diagnoses; it does not rewrite. See "Diagnostic only" below for why that boundary is hard.

When to use this

Use it when someone wants to know something about a text rather than change it: "is this AI," "does this read like a real person," "how human does this sound," "which parts sound machine-written," "rate the AI-ness," "audit this draft for AI tells," or "does this still sound like me." Use it even when they do not say "authenticity check" and even when the cue is oblique ("does this sound like a bot," "something about this feels generated, can you tell"). It also pairs naturally as a verification step before and after a humanizer pass.

Do not use it to help defeat a plagiarism or AI-detection system, or to tune text against a named detector. See "Scope and intended use" below; reframe such requests toward an honest read of quality and voice, which is what this skill actually delivers.

Diagnostic only: the hard boundary

This skill scores and flags. It does not rewrite, edit, paraphrase, or hand back "improved" prose, not even a single suggested replacement sentence. If the user wants the flagged prose fixed, that is the humanizer skill's job, performed as a separate, human-judged step.

The split is intentional and load-bearing. A single tool that scores text and then rewrites it to raise its own score is a detector-gaming loop: it optimizes prose against a metric instead of improving it for a reader, which is exactly the failure the humanizer skill explicitly refuses. Keeping diagnosis and transformation in separate skills, with a human deciding between them, is what prevents that loop from forming. So the correct shape is a pair, never a merge:

authenticity-check  ->  human reads the flags and decides  ->  humanizer
   (diagnose)              (judgment, not automation)          (rewrite)
        ^                                                          |
        |__________________ re-verify, fresh read ________________|

The re-verify pass is a fresh diagnosis of the rewritten text, not a continuation of the first one. This skill never carries a "target score" into a rewrite and never instructs the humanizer skill on how to move a number.

Core principle: uniformity is the signal

The reason AI prose reads as AI is not its vocabulary. It is its uniformity: even sentence lengths, even rhythm, even hedging, the same shapes resolved the same way. This is the same principle the humanizer skill is built on, read from the other side. Humanizer's job is to add genuine structural variance; this skill's job is to detect the absence of it.

Two consequences follow, and both matter for an honest score:

1. Flag the signature, not the vocabulary. A single "delve" is not a tell. Uniform rhythm across a whole passage is. Weigh recurrence and co-occurrence far above any lone word.
2. A relocated signature still counts. Text that swapped every "delve" for "explore" but kept the even rhythm is not more authentic; it just moved its fingerprint. Do not let surface-level word choice raise a score that the underlying uniformity should hold down.

Step 0: discover the baseline (do this every run, before scoring)

Decide which mode you are in. Announce it in the output header so a wrong guess is instantly correctable.

1. Is this a "sounds like me" question? If the user is asking whether the text reads like a specific author (themselves or a named writer) and a voice source is available (a pasted sample, a named well-known author, or a discovered profile file), enter voice-deviation mode. Read references/voice-matching.md for how to read a voice off a sample or profile; here you read it to measure deviation from that voice, not to reproduce it.
2. Look for a profile. Glob the working directory and up to two parent levels, in this priority order: VOICE.md, then STYLE-GUIDE.md, then voice-profile.md / .yaml / .json, then .manuscript/STYLE-GUIDE.md, then a voice or style section inside AGENTS.md or CLAUDE.md. One strong match plus a "sounds like me" intent puts you in voice-deviation mode. Several plausible matches: ask one short question to pick.
3. Otherwise, generic mode. No voice target. Score AI-authenticity against the catalog and the internal-consistency heuristics only. Do not invent a persona to measure against; an imagined target voice is its own failure.

This discovery is filesystem-generic. It interoperates with Scriveno, Pillars, or any project that keeps a voice file, without depending on any of them, and without assuming this skill is the only one installed.

Step 0b: density pre-check (match scrutiny to evidence)

Before scoring, skim the whole text once and judge how heavily it is AI-marked. Count only the dead-giveaway tells, not the weak surface signals: chat-UI contamination (pattern 31), knowledge-cutoff disclaimers (29), collaborative chatbot artifacts (28), significance inflation (1), promotional language (5), AI-vocabulary clustering (16), sycophantic tone (7), generic positive conclusion (13). Estimate roughly how many appear per 100 words, then set the scrutiny level and announce it in the output header:

• Low (about 0 to 2 per 100 words): light scrutiny, unless the rhythm is uniform and no human markers are present (the relocated-signature override below). Likely human-first text (a journal, rough notes, a real draft). Bias hard toward a high score and a near-empty flag list. Over-flagging genuine human prose is the worst error this skill can make; when density is low, restraint is the default. But low lexical density is evidence of a human only when the text also carries human markers or variance; clean vocabulary with even rhythm and no specificity is laundered prose, not a low-density human draft.
• Medium (about 3 to 5 per 100 words): standard scrutiny. Mixed authorship. Run the full catalog with the restraint of do-not-flag.md.
• High (6 or more per 100 words): full scrutiny. AI-first text. Run the full catalog thoroughly and weigh the internal-consistency heuristics fully.

This is calibration, not a number you report mechanically. One exception overrides density: any chat-UI contamination string (pattern 31) is decisive on its own and is always flagged, whatever the overall density, because its presence is near-certain confirmation rather than a weak signal.

A second exception overrides density in the other direction, the relocated-signature override. The trigger: the dead-giveaway count is Low only because the vocabulary is clean, yet the text shows uniform rhythm or a templated parallel skeleton (the same shape resolved the same way throughout) AND carries no concrete specificity or human markers (the do-not-flag.md Part 2 list: a specific detail or number, a dated reference, mixed feeling, an idiosyncratic sentence-length swing, a strong unhedged opinion, trade idiolect).

The rationale: this is a relocated signature, not a human-first draft. The slop words being gone is the laundering itself, not evidence of a person. Clean vocabulary does not buy a uniform, marker-free passage out of the low band.

The action: treat scrutiny as at least High, do not apply the low-density high-score bias, and carry the finding into scoring. Per scoring.md, uniform rhythm with no credited human markers is the Reads AI-generated band even when no catalog tell fired.

The multi-pass diagnostic

Run the passes in order. Pass 1 gathers candidates, Pass 2 has veto power over them, Pass 3 adds internal-consistency evidence, Pass 4 runs only in voice-deviation mode, and scoring comes last.

Pass 1: catalog scan

Load references/tell-patterns.md. Walk the prose against the 32-pattern catalog (six families), scoped to the scrutiny level chosen in Step 0b. For each genuine candidate, record the span (the actual quoted text, kept short), the pattern family, and what the pattern says about why it reads as AI. Do not rewrite the span; describe it. Recurrence and co-occurrence matter far more than any single instance; note clusters explicitly.

Pass 2: false-positive audit (has veto power over Pass 1)

Load references/do-not-flag.md. This pass is mandatory and it can overrule Pass 1. For every candidate from Pass 1, ask:

a. Is this a weak signal standing alone (one em dash, one tricolon, one passive, formal register, perfect grammar, a common transition)? If it does not recur or co-occur, drop it. b. Is this actually a human-writing marker (a specific concrete detail or number, mixed feeling, a dated reference, a self-corrective aside, an idiosyncratic sentence-length swing, strong unhedged opinion, trade idiolect)? If so, it is not a flag; it is positive evidence of a human author, and it should raise the score, not lower it.

When do-not-flag.md and tell-patterns.md disagree about a span, do-not-flag.md wins. A diagnosis that flagged genuine human voice as AI has failed even if it caught every real tell.

Pass 3: internal-consistency heuristics (read-only)

Beyond the catalog, look for spans that read as borrowed or pasted by comparing the text against itself. This is the network-free adaptation of stylometric-inconsistency and semantic-drift detection: no web search, no database, no external lookup. The procedure and weighting are defined in references/scoring.md; in brief, chunk the text, build a rough per-chunk profile (sentence-length distribution, register, lexical sophistication), and flag any span whose profile diverges sharply from the document's own established baseline. A sudden jump in technical sophistication or a register break that the surrounding prose does not earn is a span-level flag, separate from any catalog hit, because it suggests inserted or derivative material even when each sentence is individually clean.

Pass 4: voice deviation (voice-deviation mode only)

Read the target voice using the dimensions in references/voice-matching.md (cadence, diction, signature moves, punctuation habits, paragraph shape, stance, the avoid-list). Then flag spans that fall outside that author's distribution: a passage in a register the writer never uses, a rhythm they never produce, a move on their avoid-list. Honor the same conflict order voice-matching.md defines: an authentic author habit is not a tell for that writer, even when the generic catalog would flag it. In generic mode, skip this pass entirely.

Scoring

Compute the authenticity band and the 0-100 score from the surviving flags and the human markers found, following references/scoring.md. The score is a calibrated heuristic, never a verdict. Do not invent precision the evidence does not support.

False-positive guardrails

These are inline because every run needs them. Do not flag in isolation:

• Perfect grammar. Correctness is not a confession.
• A single em dash. Only dash frequency and monotony is a tell.
• Curly quotes or smart apostrophes. Word processors insert these for humans; never flag this in isolation.
• Formal or elevated vocabulary. Register is not origin.
• Common transitions ("however," "therefore," "for example").
• One passive sentence, one tricolon, one topic sentence.

Escalate to a flag only on recurrence or co-occurrence. When unsure, do not flag. The full treatment, including human markers to credit and per-model idiolects, is in references/do-not-flag.md; read it during Pass 2.

Anti-signature guidance

Do not develop diagnostic tics of your own. Do not always flag the first sentence. Do not force every report to a fixed number of flags. Do not score to a comfortable middle to avoid committing. Two genuinely human paragraphs deserve a high score and a near-empty flag list; ten dead-giveaway tells deserve a low one. Vary the verdict with the evidence, not with a habit. If your reports are starting to rhyme with each other regardless of input, stop and re-read the text cold.

Output contract

Always deliver the report in this exact structure. Never rewrite the text. Never include a "suggested" or "improved" version of any span, not even parenthetically. The flag describes the problem; it does not solve it.

## Authenticity report
Mode: [generic | voice-deviation vs FILENAME | voice-deviation vs pasted sample | voice-deviation vs author: NAME]
Scrutiny: [low | medium | high] (from the density pre-check)

Authenticity: [Reads human | Mixed signals | Reads AI-generated]   Score: NN/100
(higher means it reads more authentically as a person's own work)

## Flagged spans
- "short quoted span" -> [pattern family or heuristic name]: why this reads
  as AI-generated, AI-templated, or derivative. Description only, no rewrite.
  (ranked strongest evidence first; group sensibly; about 10 maximum; if there
  are none, say so plainly)

## Reads as human (deliberately not flagged)
- [something that looked like a tell but is an authentic human marker, and
  why it was credited rather than flagged]
  (one to three bullets; this section is required, even on low scores)

## Score basis
A short paragraph: what drove the score (the dead-giveaway tells found, the
internal-consistency findings, the voice deviation if applicable), and the
single biggest factor. Name what would most change the score.

## Caveats
This is a heuristic read, not proof of authorship. It targets and names no
detector. A high score is not a guarantee of human authorship; a low score is
not an accusation against a person. Human judgment is required to act on it.

## Next step
If the user wants the flagged prose improved, state plainly that this skill
does not rewrite, and that the fix is the separate humanizer skill, applied as
a human-judged step, after which this skill can re-verify with a fresh read.
Do not perform the rewrite here and do not hand over a target score.

The "Reads as human" and "Caveats" sections are not decoration. They are forcing functions: naming what you correctly credited as human makes over-flagging visible, and the caveat keeps the score from being read as a verdict. If a text is already essentially human, the correct output is a high score, a near-empty flag list, and a clear "Reads as human" explanation.

Scope and intended use

This skill exists to give an honest read of how authentically text reads as a person's work, and to point at the weak spots. It is not designed or tuned to defeat plagiarism checkers or AI-detection systems, and it deliberately names and optimizes against none. If a request is framed as getting AI-written work past a graded or contractual assessment, do not adopt that framing; give the honest diagnostic instead, which is what this skill does well.

The diagnostic-only boundary is itself part of the scope guarantee. Because this skill never rewrites and never carries a score into a transformation, it cannot be turned into a score-then-rewrite optimization loop. That loop is detector-gaming, the humanizer skill refuses it for the same reason, and keeping the two skills separate with a human in between is what makes the refusal hold even when both are installed together.

Reference files

Read these on demand, not upfront:

• references/tell-patterns.md during Pass 1. The 32-pattern catalog in six families. A vendored, synced copy of the humanizer repo's canonical file; see its header.
• references/do-not-flag.md during Pass 2. False positives, human markers to credit, model idiolects, hard stop conditions. Vendored and synced; see its header.
• references/voice-matching.md during Pass 4, in voice-deviation mode only. How to read a voice off a sample or profile. Vendored and synced; see its header.
• references/scoring.md for the band and 0-100 rubric and the internal-consistency heuristic definitions. Native to this repo.
• references/examples.md when you are unsure what good output looks like. Worked diagnostic runs in the exact output contract. Native to this repo.