/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.
