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 Korean (matching their working language).
- All manuscript edits are in English.
- Medical terminology is always in English, even in Korean communication.

## Reference Files

- **Pattern reference**: `${CLAUDE_SKILL_DIR}/references/ai_patterns.md` -- full 24-pattern list with expanded examples for medical/radiology manuscripts (Pattern 19–21 are senior-MA-reviewer red flags; Pattern 22–24 are response-to-reviewers letter patterns)
- **Source material**: Based on matsuikentaro1/humanizer_academic and Wikipedia: Signs of AI writing

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 24 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 24 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.
8. **Use active voice** where natural. "We analyzed" rather than "Analysis was performed."

**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 |

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

### Phase 4: Verify

Re-scan the rewritten text using the same 24 patterns.

**Output: Verification Report**

```
## Verification Report

| Metric | Before | After |
|--------|--------|-------|
| Total instances | 23 | 4 |
| Density (per 1000 words) | 8.2 | 1.4 |
| HIGH severity patterns | 3 | 0 |
| MEDIUM severity patterns | 5 | 2 |

Remaining issues:
- Pattern 17 (hedging): 2 instances remain -- appropriate for the evidence level.

Verdict: PASS (density < 2.0)
```

If the density remains above 2.0, run another fix-verify cycle (max 3 rounds).

---

## The 24 Detection Patterns

### Content Patterns

| # | Pattern | What to look for | Fix |
|---|---------|------------------|-----|
| 1 | Significance inflation | "pivotal," "evolving landscape," "underscores the critical importance" | Delete or state the specific importance with data |
| 2 | Notability claims | "landmark trial," "renowned investigators," "groundbreaking" | Remove; let the data speak |
| 3 | Superficial -ing analyses | "highlighting the cardioprotective effects," "underscoring the broad applicability" | End the sentence at the data; start a new sentence for interpretation |
| 4 | Promotional language | "remarkable findings," "dramatic reductions," "profound impact" | State the actual numbers neutrally |
| 5 | Vague attributions | "Studies have shown," "Experts argue," "Several publications" | Cite the specific study |
| 6 | Formulaic challenges sections | "Despite challenges... future outlook... continues to provide" | State specific limitations factually |

### Language Patterns

| # | Pattern | What to look for | Fix |
|---|---------|------------------|-----|
| 7 | AI vocabulary words | Additionally, crucial, delve, enhance, fostering, pivotal, showcase, tapestry, underscore, landscape (abstract) | Delete or replace with plain English |
| 8 | Copula avoidance | "serves as," "stands as," "represents a" | Use "is" |
| 9 | Negative parallelisms | "not only X but also Y" | "X and Y" |
| 10 | Rule of three o