/humanize — AI-voice audit (detect-and-flag)

Read the target file (or all paper-like files), audit for the canonical AI-voice tells in academic prose, and write a structured report. The skill does not rewrite. The author edits.

Why this skill exists

Referees and editors increasingly recognise AI-generated prose. The tells are not stylistic preferences — they're statistically conspicuous patterns the LLM training distribution produces at higher rates than human academic writers. Five reasons to audit before submission:

1. Reviewer suspicion is a tax. Even good substance pays a credibility tax if the prose reads as AI-drafted.
2. Journal policy is tightening. A growing number of venues require disclosure or prohibit AI-drafted text.
3. AI tells signal weak content. Boilerplate transitions ("Moreover", "It is important to note") almost always cover up logical gaps the author didn't think through.
4. You are not the tells. Even authors who use AI tools heavily can preserve their own voice by stripping the model's lexical fingerprint.
5. The fix is cheap once you can see it. The cost is detection, not rewriting — once the report flags the tells, removal is mechanical.

What this skill is NOT

• Not a rewriter. No --rewrite mode. Auto-rewriting AI tells degrades prose quality (cross-vendor research finding); the author preserves voice by editing manually.
• Not a substance reviewer. Use /review-paper for argument structure, identification, citations.
• Not a grammar checker. Use /proofread for grammar, typos, overflow, citation format.
• Not a fact-checker. Use /verify-claims for Chain-of-Verification fact-checking of citations and numeric claims.

/humanize is the voice lens. Run it alongside the others — none of them substitute.

When to use

• Before journal submission.
• Before posting a working paper / preprint / SSRN draft.
• After any AI-assisted prose generation (R&R response drafts, lit-review synthesis, abstract revisions).
• As a self-discipline pass after long writing sessions — your own writing drifts toward LLM patterns when you stare at LLM output all day.

When NOT to use

• On .bib, .R, or other non-prose files — the detectors are tuned for academic prose.
• On code comments — the tells are different.
• On UI/UX copy — voice norms diverge.

Detection categories

The humanize-auditor agent checks these category groups:

1. BOILERPLATE TRANSITIONS

High-confidence AI tells when they appear sentence-initial or mid-paragraph as connective tissue:

• Moreover, / Furthermore, / Additionally, / In addition,
• It is important to note that / It is worth noting that / Notably,
• In conclusion, / In summary, / To summarise,
• On the other hand, (when not contrasting two named things)
• Building on this, / Building upon this,
• As we can see, / As is evident, / Indeed, (stacked)

Severity: HIGH if more than 1 per 1000 words. MED if 1 per 2000 words. LOW if rare but present.

2. AI-CLICHÉ LEXICON

Words and phrases statistically over-represented in LLM output relative to academic prose:

• "navigate the complexities", "navigate the landscape"
• "delve into", "delve deeper into"
• "tapestry of", "rich tapestry"
• "robust framework", "comprehensive framework", "holistic framework"
• "comprehensive approach" / "multifaceted approach" / "nuanced approach" (especially when stacked)
• "leverage" (as a verb in non-finance / non-engineering contexts)
• "in today's X] landscape" / "in today's rapidly evolving"
• "play a crucial role" / "play a pivotal role" / "play a significant role"
• "shed light on"
• "underscore the importance" / "highlight the importance"
• "It is essential to" / "It is crucial to"

Severity: HIGH on a paper's first three pages (abstract, intro). MED elsewhere.

3. EM-DASH AND PUNCTUATION OVERUSE

• Em-dash overuse — more than 3 em-dashes per paragraph is a tell.
• Semicolon stacks — three or more semicolons in a single paragraph.
• Triple-Oxford-comma constructions — lists of three with deliberate parallelism repeated paragraph-to-paragraph.

Severity: MED. Em-dashes are a legitimate authorial choice; flag overuse, not all use.

4. SYMMETRIC PARAGRAPH SHAPES

Paragraphs with the same micro-architecture: topic sentence → three examples → summarising clause. Repeated across consecutive paragraphs is the AI tell — not the shape itself.

Detection: flag any three-paragraph window where each paragraph fits the topic→examples→summary cadence.

Severity: MED if 3-paragraph window; HIGH if 5+ paragraph stretch.

5. TRICOLON ABUSE

"X, Y, and Z" three-element lists are a legitimate rhetorical device. Tells are:

• More than 4 tricolons per page.
• Tricolons used for items that could naturally be 2 or 4.
• Adjective tricolons stacked ("clear, concise, and compelling"; "rigorous, robust, and reliable").

Severity: LOW if rare; MED if patterned.

6. HEDGING STACKING

Stacked epistemic hedges in single sentences:

• "might potentially be argued"
• "could possibly suggest"
• "may arguably"
• "perhaps potentially"

Severity: HIGH — these are almost never authorial choices; they're LLM uncertainty-management.

7. "NOT ONLY X, BUT ALSO Y" FRAMES

Used sparingly, this is a legitimate construction. AI tells:

• More than 2 per paper.
• Used when X and Y are not actually parallel.
• Used as paragraph openers.

Severity: MED.

8. FORMULAIC OPENERS

• Section openers of the form "This paper / chapter / section / analysis] does X]."
• Paragraph openers that re-state the section title.
• Abstract opening with "In this paper, we..." (legitimate in some sub-fields; flag for review where it's atypical, e.g., AER abstracts rarely use it).

Severity: LOW unless every section starts this way.

9. HYPHENATION EXCESS

Long chains of compound modifiers as a paragraph signature:

• "data-driven", "evidence-based", "well-suited", "well-established", "long-standing" — fine individually; flag if three or more appear in a single paragraph.

Severity: LOW.

10. SYCOPHANCY / SELF-IMPORTANT FRAMING

• "This important contribution"
• "This significant finding"
• "Our novel approach"
• Self-citation as "groundbreaking" / "pioneering"

Severity: HIGH — these read as AI-generated promotional copy; referees will react badly.

Steps

1. Identify files to audit:

• If $ARGUMENTS starts with a filename: audit that file only.
• If $ARGUMENTS is all: audit all .qmd, .tex, .md files in Slides/, Quarto/, root, and master_supporting_docs/.
• Skip .bib, .R, .py, code files, and any file under scripts/.

1. Parse --severity flag (default: report all).

• --severity low → report all findings.
• --severity med → suppress LOW findings.
• --severity high → report only HIGH findings.

1. For each file, launch the humanize-auditor agent with the 10 detection categories.

1. Receive structured report from the agent. Format per finding:

line N | category | severity | current text | suggested rewrite or "remove"

1. Write report to quality_reports/audits/humanize_<filename>_report.md. Include:

• Per-category counts (HIGH / MED / LOW)
• Per-finding table
• Summary recommendation (rough thresholds):
• > 8 HIGH findings per 1000 words: prose reads as AI-drafted. Author should rewrite the affected sections, not patch.
• 5–8 HIGH per 1000 words: substantial AI voice. Strip the tells before submission.
• < 5 HIGH per 1000 words: light cleanup; mostly cosmetic.

1. Present summary to user:

• Total findings per category
• Most concentrated paragraphs (top 3)
• Action recommendation (rewrite vs. strip vs. cosmetic)

Pairings

| Situation | Do | |---|---| | When you've drafted prose with AI assistance | Run /humanize before submission. Pair with /proofread (grammar) and /verify-claims (citations). | | When you wrote in your own voice | Run /humanize anyway — your own prose drifts toward LLM patterns after long sessions of AI-assisted work. | | Submission-ready review | /review-paper --peer [journal] --variance 3 for substance, /humanize for voice, /verify-claims for facts. |

Anti-pattern: no --rewrite mode

We deliberately do not ship /humanize --rewrite. Cross-vendor research (Cursor / Aider community findings; cited in the v1.9.0 plan) finds that auto-rewriting prose to strip AI tells degrades quality more often than it improves it — the rewriter introduces its own AI tells. The detect-and-flag pattern preserves authorial voice; the cost is your editing time, which is exactly the cost we want to pay.

If you find yourself reaching for an auto-rewriter, that's the signal to rewrite the paragraph from scratch — not to patch the tells one by one.

Output

• Report at quality_reports/audits/humanize_<filename>_report.md (that subdirectory is gitignored).
• Summary to the conversation: counts per category, top concentrated paragraphs, action recommendation.
• No file edits. The user reads the report and applies changes manually.

Respect a documented voice profile

If voice-profile.md exists at the repo root, read it first. A habit the author has declared deliberate — frequent em-dashes, first person, a particular connective — is not a finding. Flagging a documented preference as an AI tell is a false positive, and false positives erode the report's authority faster than misses do.

Build one with /voice-profile. This skill says what to remove; that one says what to write toward.

What this skill cannot do (v2.5)

/humanize finds surface tells — boilerplate transitions, the AI-cliché lexicon, hedging stacks, symmetric paragraph shapes. Fixing them improves readability, which is worth doing whoever wrote the text.

It does not make prose stop reading as machine-generated to a detector. An article polished through several rounds of surface de-AI-ing was submitted to Pangram, a neural AI-text detector, and came back 100% AI-written. Those detectors classify on the token-level statistics of LLM generation, which survive any transformation the model applies — because every transformation is still LLM-generated text.

So: a clean report here means the prose reads well. It does not mean it reads human. If provenance matters, the author writes the load-bearing sentences and measures with a real detector. See writing-with-ai.md.