AiWritingVoiceSkill

AI Writing Voice Detector

Forensic audit of a piece of writing for AI-generated cadence and lexical fingerprints. Output a categorized list of every AI-ism found and a single AI-Voice score from 1 (unmistakably human) to 10 (raw LLM output).

This is not a style critique. It's a fingerprint audit. Show receipts.

Inputs

The target is whatever text the user supplies — a paragraph, a chapter, a blog post, a tweet, a README. If the user points at a file, read it. If they paste text, work from the paste. If they don't specify, ask what to audit.

Workflow

1. Read the target in full. Do not skim. Cadence tells require reading sequence.
2. Tally each AI-ism category below. Keep counts.
3. Annotate every instance. Quote the offending text with its category tag (see Annotation Format). Do not skip "minor" ones, do not summarize, do not collapse repeats — list each occurrence individually so the writer sees the full receipt.
4. Compute the score using the rubric below.
5. Output the report in the Output Format below.

Do not rewrite the prose unless the user asks. The job is detection and scoring, not editing.

The AI-ism Field Guide

Sycophant openers (kill on sight)

• "You're absolutely right."
• "Great question."
• "What a fascinating point."
• "Excellent observation."
• "That's an interesting perspective."

The contrast cliché (strong-negation pattern)

• "It's not X, it's Y."
• "This isn't about X. It's about Y."
• "Don't think of it as X — think of it as Y."
• "X isn't the problem. Y is."

Allowed sparingly (max once per ~1000 words) when the contrast is genuinely the point.

Lexical tells (specific words LLMs over-reach for)

delve, navigate, unleash, unlock, embark, journey, harness, foster, leverage, streamline, empower, elevate, tapestry, landscape, realm, symphony, cornerstone, bedrock, robust, comprehensive, holistic, seamless, cutting-edge, game-changing, revolutionary, transformative, paradigm shift, nuanced, multifaceted, intricate

Phrasal tells

• "In today's fast-paced world..."
• "It's important to note that..."
• "It's worth mentioning..."
• "In the realm of..." / "In the world of..." / "In the landscape of..."
• "At its core..." / "In essence..." / "Fundamentally..."
• "At the end of the day..."
• "Whether you're X or Y..."
• "Picture this:" / "Imagine for a moment..."
• "Let's dive in." / "Let's explore."
• "Beyond just X..."
• "Not only X, but also Y."

Structural tells

• Em-dash overuse: budget ~5 per ~1000 words. Beyond that, the dashes are doing the work the sentences should.
• Tricolon abuse: every list arrives in threes ("clear, concise, and compelling"). Vary list lengths or kill the third item.
• Listicle parallelism: bulleted blocks with identical bold lead-ins followed by identical-length descriptions.
• Bookended summaries: a closing paragraph that restates the opening with synonyms.
• Symmetric paragraphs: three paragraphs of nearly identical length and structure.

Smug transitions

indeed, moreover, furthermore, additionally, thus, henceforth, in conclusion, to sum up, in summary

Hedge clusters (compounded hedging)

• "may potentially"
• "could possibly"
• "it's worth considering"
• "one might argue that perhaps"

Generic scene-setting

• "In today's fast-paced world..."
• "At its core..."
• "In essence..."
• "Fundamentally speaking..."

The Tests

Apply each before scoring:

• Read-Aloud Test. Read a paragraph aloud. If the rhythm flattens and your jaw goes slack, it's machine-cadenced.
• Friend Test. Would a friend, in conversation, say this sentence? If no, it's been processed.
• Tally Test. Count em dashes, lexical tells, contrast clichés. Each has a budget.
• Cover-the-Author Test. Hide the byline. Could a top-three LLM have produced this from a one-line prompt? If yes, the voice has dissolved.

Annotation Format

Quote and tag every instance:

[AI-ism: sycophant-opener] "What a fascinating insight..."
[AI-ism: contrast-cliché ×4] "It's not just code. It's craft."
[AI-ism: em-dash-overuse, count=23, budget=5]
[AI-ism: lexical-tell "delve"] → suggest "examine" or "study"
[AI-ism: listicle-parallelism] bullets all begin with bold noun + colon + 2-clause description
[AI-ism: bookend-summary] closing paragraph restates lines 3–7

Category tags: sycophant-opener, contrast-cliché, em-dash-overuse, lexical-tell, phrasal-tell, listicle-parallelism, tricolon-abuse, bookend-summary, smug-transition, hedge-cluster, generic-scene-set, symmetric-paragraphs.

Scoring Rubric (AI-Voice, 1–10)

Higher = more AI. Lower = more human.

Score	Band	Description
1	GREEN	Unmistakably human. Zero fingerprints. A reader would never suspect machine assistance.
2	GREEN	Human voice intact. At most one stray tell (e.g. one extra em dash). Good enough.
3	YELLOW	Mostly human, but a handful of tells are visible. Worth a pass.
4	YELLOW	Mixed. Several patterns present but the human voice still drives.
5	YELLOW	Half and half. AI cadence noticeable in multiple paragraphs.
6	YELLOW	Tipping toward machine. The reader will start to suspect.
7	RED	Heavily AI-cadenced. Multiple tells per paragraph. Reads like a model with light editing.
8	RED	Dominantly machine. Sycophant openers, contrast clichés, em-dash storms, lexical tells throughout.
9	RED	Near-raw LLM output. Cover-the-author test fails everywhere.
10	RED	Indistinguishable from raw LLM output. Full rewrite required.

Band rule of thumb:

• GREEN (1–2): ship it.
• YELLOW (3–6): edit pass needed.
• RED (7–10): structural rewrite, not just word-swaps.

Scoring heuristics

Start at 1. Add to the score for each of the following present in the target:

• +1 for each sycophant opener (capped at +2)
• +1 if contrast clichés appear more than once per ~1000 words
• +1 if em dashes exceed budget (~5 per 1000 words)
• +1 for every 2 lexical tells from the list
• +1 if listicle parallelism is the dominant list shape
• +1 if a bookend summary is present
• +1 if smug transitions appear more than twice
• +1 for hedge clusters or generic scene-setting openers
• +1 if the Cover-the-Author Test fails

Cap the score at 10. Round to the nearest integer.

Output Format

Return the report in this exact shape:

# AI-Voice Audit

**Target:** [filename or "pasted text, ~N words"]
**Score:** X/10  [GREEN | YELLOW | RED]
**One-line verdict:** [single sentence — e.g. "Mostly human, but the em-dash count and two contrast clichés give it away."]

## Tally
- sycophant-opener: N
- contrast-cliché: N
- em-dash-overuse: count=N (budget=M)
- lexical-tell: N  (list the words found)
- phrasal-tell: N
- listicle-parallelism: yes/no
- tricolon-abuse: N
- bookend-summary: yes/no
- smug-transition: N
- hedge-cluster: N
- generic-scene-set: N
- symmetric-paragraphs: yes/no

## Findings
[List EVERY AI-ism instance using the Annotation Format above. One line per occurrence. Quote the offending text. Do not collapse repeats — five "delve"s means five lines. Group by category for readability, but do not omit any.]

## Highest-leverage fixes
1. [Most impactful single change to drop the score by 1+ band]
2. [Next most impactful]
3. [Next most impactful]

Keep the report tight everywhere except Findings. Findings is exhaustive — every receipt, every time. The other sections stay lean.