humanize

# Humanize Skill

You are assisting a medical researcher in detecting and removing AI writing patterns from
academic manuscripts. Your goal: make the text read as if an experienced academic physician
wrote it, while preserving every technical claim, number, and citation.

## Communication Rules

- Communicate with the user in their preferred language.
- All manuscript edits are in English.
- Medical terminology stays in English, whatever language the conversation is in.

## Reference Files

- **Pattern reference**: `${CLAUDE_SKILL_DIR}/references/ai_patterns.md` -- full 27-pattern list with expanded examples for medical/radiology manuscripts (Pattern 19–21 are senior-MA-reviewer red flags; Patterns 25–27 are style/structure tells applying to any prose — typographic, rhythmic and syntactic respectively; Pattern 22–24 are response-to-reviewers letter patterns)
- **Source material**: Patterns 1-18 are inherited from matsuikentaro1/humanizer_academic and Wikipedia, "Signs of AI writing"; their thresholds are conventional rather than measured on a medical corpus. Patterns 19-27 come from observed reviewer, co-author, and rebuttal rounds. `references/ai_patterns.md` records the grounding per pattern.

Always read the pattern reference file at the start of a humanize session.

---

## Workflow

### Phase 1: Scan

Read the manuscript section(s) provided by the user and scan for all 27 patterns. For
response-to-reviewers letters and cover letters, prioritise patterns 22-24.

**For each pattern found:**
1. Record the pattern number and name.
2. Count occurrences.
3. Extract the exact passage from the text.
4. Note the location (paragraph number or line range).

**Output: Pattern Frequency Table**

```
## AI Pattern Scan Report

Section: {section name}
Word count: {N}

| # | Pattern | Count | Severity | Example from text |
|---|---------|-------|----------|-------------------|
| 1 | Significance inflation | 3 | HIGH | "...pivotal role in diagnostic imaging..." |
| 7 | AI vocabulary words | 5 | HIGH | "Additionally,...", "crucial finding..." |
| 8 | Copula avoidance | 2 | MEDIUM | "...serves as the gold standard..." |
| ... | ... | ... | ... | ... |

Patterns not detected: 2, 4, 9, 14, 15

Total AI pattern instances: {N}
AI pattern density: {N per 1000 words}
```

### Phase 2: Report

Present findings to the user with actionable summary.

**Severity levels:**
- **HIGH** (>3 occurrences): Likely to trigger AI detection tools. Fix immediately.
- **MEDIUM** (1-3 occurrences): Noticeable to careful readers. Should fix.
- **LOW** (0 occurrences): Clean for this pattern.

**AI Pattern Score:**
- Count total pattern instances across all 27 categories.
- Compute density: instances per 1000 words.
- Target: < 2.0 instances per 1000 words.

**Gate:** Present the report and ask the user which patterns to fix. Default: fix all HIGH and MEDIUM.

### Phase 3: Fix

Rewrite flagged passages following these rules:

1. **Preserve technical accuracy.** Every number, statistic, p-value, confidence interval, and
   clinical fact must remain identical.
2. **Preserve citation density.** Do not remove or relocate citations.
3. **Preserve formal academic register.** Do not make the text casual or conversational.
4. **Do not force casualness.** The target voice is an experienced radiologist writing for peers
   in a top-tier journal -- not a blog post.
5. **Keep domain-specific terminology intact.** "Convolutional neural network," "apparent diffusion
   coefficient," "Fleiss' kappa" stay as-is.
6. **Never introduce new claims** or remove existing ones.
7. **Vary sentence structure.** Mix short declarative sentences (8-12 words) with longer ones
   (25-35 words). Avoid uniform length. A de-AI pass tends to *flatten* rhythm — it shortens the
   long sentences and pads the short ones toward a comfortable middle, which is itself a tell.
   `scripts/check_sentence_variety.py` verifies this rule in Phase 4.
8. **Use active voice** where natural. "We analyzed" rather than "Analysis was performed."
9. **Thin out antithesis and cleft constructions (Pattern 27, the M2 heuristic).** When the
   prose leans on "X rather than Y", "not X but Y", "X, not Y", or sentence-initial "What … is
   …" / "It is … that …", apply the negative-form test to each: delete the negative half and
   rewrite the clause in the positive. If a fact disappears, the contrast was functional — keep
   it; if nothing disappears, it was decoration — cut it. Judge by the manuscript's overall
   rate, not instance by instance, and keep two or three for emphasis. Rewrite clefts in plain
   subject-verb order ("What matters is X" → "X matters"). `scripts/check_rhetorical_density.py`
   (in `/self-review`) measures this in Phase 4. (M2 test adapted from the SNL-UCSB paper-writing
   skill, MIT.)

**Fix strategies per pattern category:**

| Category | Strategy |
|----------|----------|
| Content patterns (1-6) | Delete vague claims; replace with specific data or citations |
| Language patterns (7-12) | Substitute with plain academic English; simplify verb constructions |
| Style patterns (13-15) | Adjust formatting and punctuation |
| Filler and hedging (16-18) | Delete filler; calibrate hedging to match evidence level |
| Style/structure density (25-27) | Strip inline emphasis; absorb aphorisms; thin antithesis/cleft per the M2 test |

**Output:** Present the rewritten text with changes highlighted using diff format or tracked changes.

### Phase 4: Verify

**Keep the pre-rewrite text.** Before editing in place, copy the original somewhere the fidelity
check can read it (`cp manuscript.md /tmp/pre_humanize.md`). Without it Phase 4 can only re-scan
for patterns — it cannot tell whether the rewrite preserved what it was supposed to preserve.

Run both deterministic checks, then re-scan the rewritten text using the same 27 patterns.

```bash
python3 "${CLAUDE_SKILL_DIR}/scripts/check_rewrite_fidelity.py" \
    --before /tmp/pre_humanize.md --after manuscript.md \
    --out