Humanize

/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:

• Reviewer suspicion is a tax. Even good substance pays a credibility tax if the prose reads as AI-drafted.
• Journal policy is tightening. A growing number of venues require disclosure or prohibit AI-drafted text.
• 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.
• You are not the tells. Even authors who use AI tools heavily can preserve their own voice by stripping the model's lexical fingerprint.
• 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

• 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/.

• Parse --severity flag (default: report all).
• --severity low → report all findings.
• --severity med → suppress LOW findings.
• --severity high → report only HIGH findings.

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

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

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

• Write report to quality_reports/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.

• Present summary to user:
• Total findings per category
• Most concentrated paragraphs (top 3)
• Action recommendation (rewrite vs. strip vs. cosmetic)

Pairings

| 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/humanize_<filename>_report.md (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.