Humanize

Humanize Skill

This skill only removes AI patterns; it does not perform general copy-editing, evaluate scientific quality, check journal formatting, or translate.

Read ${CLAUDE_SKILL_DIR}/references/ai_patterns.md at the start of every session, before scanning: the definitions, watch words, examples, detection greps, per-pattern fixes and the section-by-section priorities for all 27 patterns live only there.

Workflow

Phase 1: Scan

Scan the section(s) the user provides for all 27 patterns. For response-to-reviewers letters and cover letters, prioritise Patterns 22-24. For a full manuscript, follow the per-section priorities in ai_patterns.md (Section-Specific Application Guide). For each pattern found, record its number and name, the count, the exact passage, and its 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..." |
| ... | ... | ... | ... | ... |

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

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

Phase 2: Report

• Severity per pattern: HIGH (>3 occurrences), MEDIUM (1-3), LOW (0, clean).
• Density: total instances across all 27 patterns per 1000 words. Target: < 2.0.

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

Phase 3: Fix

Rewrite flagged passages with each pattern's fix from ai_patterns.md, under these rules:

1. Preserve technical accuracy. Every number, statistic, p-value, confidence interval, and clinical fact must remain identical.
2. Preserve citations. Never add, remove, or relocate a citation.
3. Keep the formal academic register of an experienced radiologist writing for peers in a top-tier journal. Never make the text casual or conversational.
4. Keep domain-specific terminology intact. "Convolutional neural network," "apparent diffusion coefficient," "Fleiss' kappa" stay as-is.
5. Never introduce new claims, remove existing ones, or change a sentence's meaning — rephrase, never reinterpret. If a passage cannot be fixed without changing its meaning, leave it and flag it for the user.
6. Use active voice where natural: "We analyzed" rather than "Analysis was performed."
7. Vary sentence structure. Mix short declarative sentences (8-12 words) with longer ones (25-35 words). 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. Thin out antithesis and cleft constructions (Pattern 27, the M2 heuristic). For each "X rather than Y", "not X but Y" or "X, not Y", apply the negative-form test: 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 ("What … is …", "It is … that …") in plain subject-verb order ("What matters is X" → "X matters"). scripts/check_rhetorical_density.py (in /self-review) measures this in Phase 4.

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 numbers and citations.

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

python3 "${CLAUDE_SKILL_DIR}/scripts/check_rewrite_fidelity.py" \
    --before /tmp/pre_humanize.md --after manuscript.md \
    --out qc/rewrite_fidelity.json --strict
python3 "${CLAUDE_SKILL_DIR}/scripts/check_sentence_variety.py" \
    --manuscript manuscript.md --out qc/sentence_variety.json

NUMBER_DRIFT, NUMBER_REASSIGNED, CITATION_DROP or CITATION_MOVED means the rewrite broke an invariant — revert that passage, redo it, and flag it for the user. EDIT_FOOTPRINT_HIGH is advisory: Patterns 6 and 18 replace whole paragraphs by design, so a correct pass over an inflated draft can exceed 60% of words changed. Read the diff and confirm the author's argument survived rather than assuming the percentage is a defect.

Known limits: the fidelity gate does not check an added or removed negation, a number written in words, a changed unit, or a direction word next to a non-percentage. A clean exit does not clear these; read the diff for them.

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). When called by another skill, return the verification report so the calling skill can check the pass/fail status.

The 27 Detection Patterns

All 27 are defined in references/ai_patterns.md. Two carry rules to apply exactly as written:

Patterns 22-24 apply only to response-to-reviewers letters and editor cover letters, not manuscript bodies. They are defined once in ai_patterns.md (Response-Letter Patterns); for authoring guidance, see the revise skill's references/r2r_voice.md.