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light-review-rebuttal

Build auditable peer-review revision and author-response packages for Light stage 13. Use after receiving reviewer comments, a decision or meta-review; when drafting a rebuttal or response letter; when triaging major/minor revisions; when simulating a pre-submission review; or when a rejection may require a user-chosen 13→3 novelty, 13→5 experiment, or 13→8 writing back-edge. Consumes the selected venue/context and real PDF facts, preserves reviewer wording, atomizes issues, binds claims/evidence/actions/provenance, separates PLANNED from DONE, checks current venue limits without borrowing another venue's rules, and emits the stage-13 gate without changing venue, manuscript, evidence, citations, figures, PDF, or passport automatically.

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git clone --depth 1 https://github.com/Light0305/Light-skills /tmp/light-review-rebuttal && cp -r /tmp/light-review-rebuttal/skills/light-review-rebuttal ~/.claude/skills/light-review-rebuttal
Después abre una sesión nueva de Claude Code; el skill carga automáticamente.

SKILL.md

# Review and rebuttal

Build a source-preserving review registry, issue matrix, revision plan,
evidence/change map, response draft, commitment ledger, unknown/failure record,
and delivery package. Treat prose generation as the last layer, not the first.

Read [`review-rebuttal-resource-map.md`](review-rebuttal-resource-map.md) before
a real run. Read
[`references/workflow_contract.md`](references/workflow_contract.md) before
creating or consuming canonical JSON. Read [`references.md`](references.md)
when selecting review/rule sources. The competitor evidence is
[`../../docs/competitors/review-rebuttal.md`](../../docs/competitors/review-rebuttal.md).

## Non-negotiable boundaries

1. Consume venue-matching's selected handoff and review context. Verify selected
   identity, `selected_at` timezone, `selection_basis`, user/delegated
   authorization, chosen candidate ID, fit/risk row, unmodified rule envelopes,
   source evidence path/as-of/source IDs, manuscript profile, and PDF
   path/hash/pages/page size/profile/compliance. Never switch venue, reorder
   tiers, or turn venue `UNKNOWN` into a fact.
2. Keep reviewer, editor, decision, and meta-review text verbatim in the
   canonical registry. Atom labels, root causes, strategies, and generated
   prose are interpretation layers; they never replace source text.
3. Record fetch time, source URL/type, round, reviewer ID, attachments and
   `AVAILABLE|UNKNOWN|UNAVAILABLE|STALE`. A 401/403/429/5xx, timeout, login,
   private invitation or network failure is `UNAVAILABLE`, not “no review.”
4. Never invent an experiment, analysis, citation, change, line number,
   reviewer identity, venue rule or result. `PLANNED` and `IN_PROGRESS` may not
   be phrased as completed. `DONE` requires a real change locator; completed
   experiment/analysis additionally requires verifiable run provenance with a
   matching SHA-256, not merely a local path.
   Before marking a response package ready, run the atom/action contract gate so
   source spans, reconstruction hashes, policy/ethics authorization and
   perspective-specific self-review are machine-checked rather than trusted.
5. Paper-writing owns manuscript claims and edits. Result-analysis owns
   evidence strength. Citation owns new-reference identity and claim support.
   Figure owns visual honesty. Typesetting owns PDF rebuild/compliance. This
   skill records and routes work; it does not impersonate those producers.
6. Stage 13 critical is narrow: only a routable root cause
   (`novelty|experiment|writing`) explicitly marked `rejection_driving=true`
   with a complete decision/meta-review/reviewer evidence envelope may become
   critical. Major labels or an overall Reject alone do not make every comment
   critical.
7. `reviewer_classify` and `reroute` produce advice only. Stop after presenting
   evidence and alternatives. Run `passport add-back-edge` only after the user
   chooses the root cause/back-edge. Never mutate the passport automatically.

## Canonical workflow

### 1. Verify upstream identity

Require:

- `light.selected_venue_handoff.v1`;
- `light.review_rebuttal_venue_context.v1`;
- `light.paper_claims.v1`;
- `light.evidence_strength.v1`;
- citation registry when any new citation is proposed;
- the actual delivered PDF and its stage-11 facts.

Run:

```bash
python scripts/review_workflow.py \
  --spec review-input.json \
  --outdir review-delivery
```

If venue identity, rule envelopes, PDF hash, compliance, claims or evidence IDs
do not agree, stop and repair the producer artifact. Do not “normalize” a
conflict away.

The selected handoff must also retain A32's audit fields: timezone-bearing
`selected_at` that is not in the future, non-empty `selection_basis`,
`decision_authority=user`, coherent `selected_by/status`, delegated
`user_authorization` when applicable,
unchanged `fit_risk`, and a readable `source_evidence.path` whose SHA-256
matches the selected handoff and whose `as_of/source_ids` cover every sourced
venue rule.

### 2. Capture reviews and decisions

For user-provided/private material, copy the text into `reviews[].raw_text`
without correction and record `reviews[].raw_sha256` plus a timezone-aware
`captured_at`; the workflow re-computes the hash and blocks future/naive capture
times. For a public OpenReview forum:

```bash
python scripts/fetch_openreview.py \
  --forum <forum-id> \
  --out openreview-capture.json
```

If the live API is unavailable but a fixed public PeerRead/OpenReview snapshot
is the declared evidence source, capture that exact commit-pinned JSON instead:

```bash
python scripts/fetch_openreview.py \
  --peerread-url <raw-fixed-commit-json-url> \
  --out peerread-capture.json
```

The capture is calibration/source evidence. Do not redistribute restricted
reviews. If capture is unavailable, continue only with material the user
provided and retain the failure record.

### 3. Atomize without erasing source

Create one atom for each distinct `request`, `claim`, `question`,
`misunderstanding`, or `editorial` item. Each atom must contain an exact
contiguous source span copied from `raw_text`, with start/end offsets, span
text and SHA-256. Also create addressable coverage units and a reconstruction
hash for the reviewer units that require a response.

Assign one root cause such as `novelty`, `experiment`, `writing`,
`clarification`, `citation`, `ethics`, `scope`, or `editorial`. Add a separate
interpretation explaining the inferred concern. If a sentence contains two
independent asks, create two atoms pointing to the same or overlapping source
span; do not paraphrase the reviewer into a new source quote.

Run the stricter losslessness/response-action gate before drafting:

```bash
python scripts/review_response_contract.py \
  --input templates/review-response-contract.example.json
```

Replace the template with the real contract. The example is intentionally
non-passing until current venue policy, ethics state and user authorization are
verified. This gate catch
light-backend-codingSkill

后端代码编写、逻辑强、安全性高、可读性好、版本控制、代码审查。当任务需要写实验代码、模型代码、数据处理代码、可视化代码、后端接口或系统逻辑时使用。要求逻辑清晰、安全、可读、可维护、便于复现/扩展/部署。支持 Git 版本管理、代码审查、注释规范、README、依赖管理、环境配置、运行说明与项目结构整理。

light-citationSkill

Verify scholarly references and claim-citation support for Light stage 10. Use when auditing a manuscript, claim map, bibliography, DOI/arXiv/PMID/ISBN/URL, BibTeX/CSL, citekeys, chimeric or fabricated citations, retraction/correction alerts, or preparing a canonical citation registry for typesetting. Builds provenance-preserving inventories, confirms metadata with independent authoritative sources, distinguishes CONFIRMED/CONFIRMED-MISSING/UNAVAILABLE/UNRESOLVED, records Crossref update direction, and emits the citation gate plus delivery artifacts.

light-competitionSkill

竞赛与项目申报材料辅助。当用户做统计建模、数学建模、互联网+、挑战杯、大创、创新创业、科研训练等项目时使用。辅助写申报书、项目计划书、商业计划书、路演 PPT、答辩稿、项目摘要、技术路线、创新点、可行性分析、市场分析、研究基础、预期成果、经费预算、团队分工。用于非论文投稿场景,可与论文/软著/专利/PPT 联动。

light-consistencySkill

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light-data-engineeringSkill

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light-figure-drawingSkill

从顶会大牛角度进行专业绘图与组图。当用户需要把规划好的图实际画出来时使用。按情况用 Python(matplotlib/seaborn/plotly/altair)、R(ggplot2)、MATLAB、Visio、Origin、LaTeX/TikZ、Illustrator、PowerPoint 等。审美统一、专业清晰、配色合理、字体规范、线条清楚、高分辨率,适合直接投稿。不仅画图,还从论文表达角度判断怎么排、怎么组、怎么标注、怎么突出重点。

light-figure-planningSkill

根据论文内容规划应该做哪些图、哪些表、插在哪里、各起什么作用。当用户需要论文图表规划时使用。图表不限于统计图,也包括数据集真实效果图、模型输出示例、案例展示、可解释性可视化等。规划框架图、技术路线图、数据集示意图、模型结构图、算法流程图、结果对比/消融/敏感性图、真实效果图、统计表/对比表等,以审稿人标准判断哪些必做、哪些冗余。

light-file-readingSkill

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