sci-review

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Sci-Review

Use this skill to produce structured literature-review writing and professional reviewer responses.

使用该技能生成结构化的文献综述文稿以及专业的评审意见回复。

Literature Review Structure

文献综述结构

Use this four-part structure unless the user requests a different journal format:

Introduction: background, problem definition, gap identification, and contribution.
Methodology: taxonomy, method classes, comparison dimensions, and performance evidence.
Challenges: phenomenon, cause, and direction. Make the problem visible before proposing a route forward.
Conclusion: distilled insights and a future roadmap.

Prefer specific evidence over broad claims. Replace vague phrases such as "significantly better" with measured comparisons when data is available.

除非用户要求采用特定期刊格式，否则请使用以下四部分结构：

引言：背景介绍、问题定义、研究缺口识别以及研究贡献说明。
方法论：分类体系、方法类别、对比维度以及性能证据。
挑战：现象描述、原因分析以及改进方向。在提出解决方案之前，需清晰呈现问题。
结论：提炼核心见解并规划未来研究路线图。

优先使用具体证据而非宽泛表述。若有数据支持，将“显著更优”等模糊表述替换为量化对比内容。

Rebuttal Structure

反驳意见结构

For each reviewer point, use:

Reviewer concern: restate the concern accurately and neutrally.
Response: answer with evidence, clarification, or a limitation acknowledgement.
Revision plan: state the exact manuscript change, including section, table, figure, appendix, or experiment when possible.

Avoid adversarial phrasing such as "reviewer misunderstood" or "the reviewer is wrong". Use constructive language such as "we will clarify this point in the manuscript" or "we agree that additional evidence would improve the presentation".

针对每条评审意见，采用以下结构：

评审关注点：准确、中立地重述评审意见。
回复内容：通过证据、解释说明或承认研究局限性来回应。
修订计划：明确说明手稿的具体修改内容，尽可能包含章节、表格、图表、附录或实验相关细节。

避免使用“评审员存在误解”或“评审员观点错误”等对抗性措辞。采用建设性语言，例如“我们将在手稿中明确这一点”或“我们认同补充证据有助于提升文稿质量”。

Validation

验证机制

The skill includes a lightweight validator. Run from the

skills/sci-review/

directory:

bash

undefined

该技能包含一个轻量级验证工具。请在

skills/sci-review/

目录下运行以下命令：

bash

undefined

From the skills/sci-review/ directory:

python scripts/validate_review_output.py --case literature-review --output output.md python scripts/validate_review_output.py --case rebuttal --output output.md python scripts/validate_review_output.py --list-golden


The validator checks required section names and banned phrases. Golden cases live in `tests/golden_cases.json`; they define expected output features rather than exact wording.


验证工具会检查是否包含必填章节名称以及是否存在禁用措辞。测试用例存储在`tests/golden_cases.json`中；这些用例定义了预期输出的特征而非具体措辞。

Best Practices

最佳实践

Read the source literature, reviewer comments, or draft before rewriting.
Preserve technical nuance. Do not invent experiments, results, baselines, or citations.
Mark uncertainty explicitly when source evidence is missing.
Keep tone professional, direct, and evidence-driven.

在重写前阅读原始文献、评审意见或草稿内容。
保留技术细节。不得虚构实验、结果、基准或引用文献。
当原始证据缺失时，需明确标注不确定性。
保持语气专业、直接且基于证据。