--- name: humanize description: Read-only audit of `.tex`, `.qmd`, or `.md` text for AI-voice tells — boilerplate transitions ("Moreover", "Furthermore", "It is important to note that"), AI-cliché lexicon ("delve", "navigate the complexities", "tapestry", "robust framework"), em-dash overuse, symmetric paragraph shapes, tricolon abuse, hedging stacking, "not only X but also Y" frames, and formulaic openers. Produces a report; does NOT rewrite. Use when user says "humanize", "does this sound like AI?", "check for AI tells", "de-AI this draft", "remove AI voice", "audit my prose for sycophancy", or before journal submission / posting a working paper. author: Claude Code Academic Workflow version: 1.0.0 argument-hint: "[filename or 'all'] [--severity low|med|high]" disable-model-invocation: true allowed-tools: ["Read", "Grep", "Glob", "Write", "Task"] --- # `/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/`. 2. **Parse `--severity` flag** (default: report all). - `--severity low` → report all findings. - `--severity med` → suppress LOW findings. - `--severity high` → report only HIGH findings. 3. **For each file, launch the `humanize-auditor` agent** with the 10 detection categories. 4. **Receive structured report** from the agent. Format per finding: ``` line N | category | severity | current text | suggested rewrite or "remove" ``` 5. **Write report** to `quality_reports/humanize__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. 6. **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__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.