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Get Started Free →Draft, audit, or revise point-by-point reviewer response letters for Nature-family manuscript revisions. Use when the user provides reviewer comments, editor decision letters, revision notes, response drafts, or asks how to respond to major/minor revision requests, rebuttal letters, response to reviewers, peer-review reports, 审稿意见回复, 逐点回复, 修回信, 大修回复, 小修回复, or 如何回复 reviewer.
.claude/skills/galaxy-dawn-nature-response/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-07 | ✗→✓ | ▲ Improved | 185% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 118% | 0% |
| case-11 | ✗→✓ | ▲ Improved | 47% | 0% |
| case-14 | ✗→✓ | ▲ Improved | 72% | 0% |
| case-15 | ✗→✓ | ▲ Improved | 103% | 0% |
Use this skill to convert editor decision letters, reviewer comments, author notes, or draft rebuttals into an auditable point-by-point response package for manuscript revisions.
The response letter is an editor-facing verification document. The goal is to show that every reviewer concern has been understood, addressed, and mapped to a concrete manuscript change, justified scientific response, or unresolved author action.
AUTHOR_INPUT_NEEDED.For response structure or tone, check the active installed skills/ml-paper-writing/references/knowledge/paper-miner-writing-memory.md under the current client's skill home. Read only relevant rebuttal and venue entries. Reviewer comments, manuscript changes, and journal instructions remain the source of truth. Do not borrow claims or copy source phrasing. If the memory is absent or has no relevant entries, continue with this skill's references.
The skill may receive:
If reviewer boundaries or comment segmentation are ambiguous, flag the ambiguity instead of inventing reviewer structure.
draft, audit, revise, triage-only, or appeal-like.E.1, then split reviewer comments with IDs such as R1.1, R1.2, and R2.1.triage-only or appeal-like.ready_to_submit, draft_with_placeholders, needs_author_input, or blocked.Unless the user asks for another format, return:
textResponse strategy summary - Decision type: - Overall posture: - Major risks: - Suggested ordering: Comment-response tracker | ID | Reviewer concern | Type | Severity | Proposed action | Missing author input | |---|---|---|---|---|---| Draft point-by-point response letter [editor-readable English response] Manuscript change checklist - [specific manuscript changes or placeholders] Missing information / risk flags - [specific unresolved items or "None"] 中文核对 - [when the user writes in Chinese; otherwise omit unless useful]
| File | Open when | |---|---| | references/intake-and-routing.md | Before drafting, to identify task mode, minimum inputs, editor IDs, readiness state, and clarifying-question need | | references/source-basis.md | You need source hierarchy, rule provenance, or policy-vs-advice boundaries | | references/response-structure.md | You need the response package format or point-by-point letter anatomy | | references/comment-taxonomy.md | You need to classify reviewer comments by category and severity | | references/action-mapping.md | You need action labels, tracker fields, and missing-input states | | references/tone-and-stance.md | You need recommended language, forbidden phrasing, or disagreement tone | | references/chinese-author-alignment.md | The user writes in Chinese or provides Chinese author notes | | references/difficult-cases.md | The comments involve impossible experiments, factual errors, conflicting reviewers, citations, statistics, compliance, transfer, or appeal-like cases | | references/qa-checklist.md | Before finalizing an output or auditing a draft response |
Use sources in this order:
If a policy detail may have changed, verify the current journal page before giving final submission advice.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 98,832 | 14,103 | -86% | 1 | 1 | 0% | 8,286 | 3,281 | -60% | 0 | 0 | — |
case-02 | fail→fail | 70,535 | 11,565 | -84% | 1 | 1 | 0% | 3,788 | 2,929 | -23% | 0 | 0 | — |
case-03 | fail→fail | 52,680 | 8,546 | -84% | 1 | 1 | 0% | 8,532 | 2,449 | -71% | 0 | 0 | — |
case-04 | pass→pass | 14,923 | 18,447 | +24% | 1 | 1 | 0% | 2,098 | 4,097 | +95% | 0 | 0 | — |
case-05 | pass→fail | 21,282 | 12,133 | -43% | 1 | 1 | 0% | 2,857 | 3,198 | +12% | 0 | 0 | — |
case-06 | pass→pass | 19,592 | 23,283 | +19% | 1 | 1 | 0% | 2,452 | 4,767 | +94% | 0 | 0 | — |
case-07 | fail→pass | 14,797 | 26,782 | +81% | 1 | 1 | 0% | 1,906 | 5,425 | +185% | 0 | 0 | — |
case-08 | pass→pass | 10,607 | 17,621 | +66% | 1 | 1 | 0% | 1,659 | 3,852 | +132% | 0 | 0 | — |
case-09 | fail→pass | 14,678 | 18,249 | +24% | 1 | 1 | 0% | 1,860 | 4,058 | +118% | 0 | 0 | — |
case-10 | pass→pass | 16,430 | 19,986 | +22% | 1 | 1 | 0% | 2,295 | 4,134 | +80% | 0 | 0 | — |
case-11 | fail→pass | 14,584 | 12,047 | -17% | 1 | 1 | 0% | 2,314 | 3,403 | +47% | 0 | 0 | — |
case-12 | pass→pass | 17,365 | 22,150 | +28% | 1 | 1 | 0% | 2,466 | 4,749 | +93% | 0 | 0 | — |
case-13 | pass→pass | 13,842 | 15,420 | +11% | 1 | 1 | 0% | 1,946 | 3,489 | +79% | 0 | 0 | — |
case-14 | fail→pass | 14,617 | 13,623 | -7% | 1 | 1 | 0% | 1,900 | 3,267 | +72% | 0 | 0 | — |
case-15 | fail→pass | 11,098 | 10,598 | -5% | 1 | 1 | 0% | 1,373 | 2,783 | +103% | 0 | 0 | — |
case-16 | pass→pass | 24,259 | 32,008 | +32% | 1 | 1 | 0% | 2,188 | 4,251 | +94% | 0 | 0 | — |
case-17 | pass→pass | 8,978 | 8,580 | -4% | 1 | 1 | 0% | 1,182 | 2,467 | +109% | 0 | 0 | — |
case-18 | pass→pass | 9,197 | 16,287 | +77% | 1 | 1 | 0% | 1,432 | 4,005 | +180% | 0 | 0 | — |
case-19 | fail→pass | 14,089 | 15,528 | +10% | 1 | 1 | 0% | 2,180 | 3,506 | +61% | 0 | 0 | — |
case-20 | pass→pass | 7,784 | 6,788 | -13% | 1 | 1 | 0% | 1,001 | 2,092 | +109% | 0 | 0 | — |
case-21 | pass→pass | 7,978 | 10,680 | +34% | 1 | 1 | 0% | 1,091 | 2,574 | +136% | 0 | 0 | — |
case-22 | pass→pass | 14,678 | 18,714 | +27% | 1 | 1 | 0% | 1,924 | 4,117 | +114% | 0 | 0 | — |
case-23 | fail→fail | 11,567 | 13,475 | +16% | 1 | 1 | 0% | 1,638 | 3,243 | +98% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted. The headline lift of +22 percentage points is the difference between those two pass rates over the 23 comparable cases. 2 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
| Model | Method | Date | Lift |
|---|---|---|---|
| gemini-3.6-flash | verified | 8/9/2026 | +9% |
Other measured skills in the registry, with their headline benchmark lift.