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peer-review-response-drafter同行评审回复起草者

Agent Skill

peer-review-response-drafter 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:peer-review-response-drafter(同行评审回复起草者)
来源仓库:https://github.com/aipoch-ai/peer-review-response-drafter
安装命令:
openclaw skills install peer-review-response-drafter
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

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openclaw skills install peer-review-response-drafter

简介

协助起草专业的同行评审回复信,提升投稿效率与沟通质量。

  • 当收到审稿意见时自动触发,基于反馈内容生成回应草稿。
  • 输出包含逐条回复、修改说明与致谢的标准学术格式。
  • 仅作初稿参考,最终发布前需经作者审阅修改。peer-review-response-drafter 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 不替代人工判断,重点在于节省文案撰写时间。

SKILL.md

name
peer-review-response-drafter
description
Assist in drafting professional peer review response letters. Trigger
version
1.0.0
category
Research
tags
[]
author
AIPOCH
license
MIT
status
Draft
risk_level
Medium
skill_type
Tool/Script
owner
AIPOCH
reviewer
last_updated
2026-02-06

Peer Review Response Drafter

Assist researchers in crafting professional, polite, and effective responses to peer reviewer comments for academic journal submissions.

Overview

This skill parses reviewer comments, drafts structured responses, and adjusts tone to ensure:

  • Professional and courteous language
  • Clear point-by-point addressing of concerns
  • Constructive framing of disagreements
  • Consistent academic writing style

When to Use

  • Responding to peer reviewer comments after paper revision
  • Preparing author response letters for journal resubmission
  • Addressing major/minor revision requirements
  • Drafting rebuttal letters for conference submissions
  • Converting informal notes into formal response language

Workflow

Step 1: Parse Input

Collect and structure the following:

  • Reviewer comments: Original text from reviewers (often numbered/sectioned)
  • Manuscript context: Title, journal name, revision round (if applicable)
  • Author changes: Brief notes on what was modified in response to each comment
  • Tone preference: Formal academic / diplomatic / assertive (default: diplomatic)

Step 2: Structure Response Letter

Standard academic response letter format:

Dear Editor and Reviewers,

Thank you for your constructive feedback on our manuscript titled 
"[Title]" submitted to [Journal]. We have carefully addressed all 
comments and revised the manuscript accordingly. Below is our 
point-by-point response to each reviewer's comments.

Reviewer #1:
[Numbered responses]

Reviewer #2:
[Numbered responses]

...

Sincerely,
[Authors]

Step 3: Draft Individual Responses

For each reviewer comment, generate a response containing:

  1. Acknowledgment: Thank the reviewer for the observation
  2. Action taken: Describe the change made (if applicable)
  3. Location indicator: Page/line number where change appears
  4. Optional rationale: Brief explanation if no change was made

Response Templates

Accepting a suggestion:

Comment: The methodology section lacks detail on data preprocessing.

Response: We thank the reviewer for this important observation. 
We have expanded the methodology section to include detailed 
descriptions of data preprocessing steps, including normalization, 
outlier removal, and feature selection procedures (Page 5, Lines 120-135).

Partial acceptance with modification:

Comment: The authors should use Method X instead of Method Y.

Response: We appreciate the reviewer's suggestion. While Method X 
is indeed widely used, we found that Method Y is more appropriate 
for our specific dataset due to [brief rationale]. However, we have 
added a comparative discussion of both methods in the revised 
manuscript (Page 8, Lines 200-210) to acknowledge this alternative 
approach.

Politely declining:

Comment: The authors should remove Figure 3 as it seems redundant.

Response: We thank the reviewer for this suggestion. Upon careful 
consideration, we believe Figure 3 provides essential visual 
support for the key finding discussed in Section 4.2. To enhance 
clarity, we have revised the figure caption to better emphasize 
its unique contribution (Page 10, Figure 3 caption).

Step 4: Tone Adjustment

Adjust language based on context:

ToneUse CaseExample Phrasing
DiplomaticGeneral revisions"We thank..." / "We appreciate..." / "We have revised..."
AssertiveDefending methodology"We respectfully note..." / "Our approach is justified because..."
GratefulMajor improvements"We are grateful for..." / "This significantly improved..."

Input Format

Accept multiple input formats:

  • Copy-pasted reviewer comments
  • PDF extracted text
  • Structured JSON with comment IDs
  • Markdown with sections

Output Format

Returns a complete response letter with:

  • Proper salutation and closing
  • Numbered responses matching reviewer comments
  • Inline citations to manuscript locations
  • Professional academic tone throughout

Usage Example

User: Help me draft a response to these reviewer comments:

Reviewer 1:
1. The introduction should better motivate the problem
2. Figure 2 is unclear
3. Have you considered Smith et al. 2023?

My changes:
1. Added motivation paragraph
2. Redrew Figure 2 with clearer labels
3. Added citation and discussion

Journal: Nature Communications

Parameters

ParameterTypeRequiredDefaultDescription
--interactiveflagNo-Interactive mode: Guided wizard with prompts (uses input()). Recommended for first-time users or complex responses
--input-filestrNo-Path to reviewer comments file (automation mode)
--outputstrNo-Output file path for response letter
--tonestrNo"diplomatic"Response tone: "diplomatic", "formal", or "assertive"
--formatstrNo"markdown"Output format: "markdown", "plain_text", or "latex"
--include-diffboolNotrueWhether to summarize changes made

Usage Modes:

  • Interactive Mode: Use --interactive for guided setup with prompts (recommended for first-time users)
  • File Mode (Recommended for automation): Use --input-file with pre-prepared reviewer comments

Technical Notes

  • Difficulty: High - Requires understanding of academic norms, context-aware tone adjustment, and nuanced handling of criticism
  • Limitations: Does not verify factual accuracy of responses; human review required for technical content
  • Safety: No external API calls; processes text locally

References

  • references/response_templates.md - Common response patterns
  • references/tone_guide.md - Academic tone guidelines
  • references/examples/ - Sample response letters

Quality Checklist

Before finalizing, verify:

  • [ ] Every reviewer comment has a corresponding response
  • [ ] Responses are numbered/lettered consistently with comments
  • [ ] All changes are referenced with page/line numbers
  • [ ] Disagreements are framed constructively
  • [ ] No defensive or confrontational language
  • [ ] Professional tone maintained throughout

Risk Assessment

Risk IndicatorAssessmentLevel
Code ExecutionPython/R scripts executed locallyMedium
Network AccessNo external API callsLow
File System AccessRead input files, write output filesMedium
Instruction TamperingStandard prompt guidelinesLow
Data ExposureOutput files saved to workspaceLow

Security Checklist

  • [ ] No hardcoded credentials or API keys
  • [ ] No unauthorized file system access (../)
  • [ ] Output does not expose sensitive information
  • [ ] Prompt injection protections in place
  • [ ] Input file paths validated (no ../ traversal)
  • [ ] Output directory restricted to workspace
  • [ ] Script execution in sandboxed environment
  • [ ] Error messages sanitized (no stack traces exposed)
  • [ ] Dependencies audited

Prerequisites

# Python dependencies
pip install -r requirements.txt

Evaluation Criteria

Success Metrics

  • [ ] Successfully executes main functionality
  • [ ] Output meets quality standards
  • [ ] Handles edge cases gracefully
  • [ ] Performance is acceptable

Test Cases

  1. Basic Functionality: Standard input → Expected output
  2. Edge Case: Invalid input → Graceful error handling
  3. Performance: Large dataset → Acceptable processing time

Lifecycle Status

  • Current Stage: Draft
  • Next Review Date: 2026-03-06
  • Known Issues: None
  • Planned Improvements:

- Performance optimization - Additional feature support

适合场景

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用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

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能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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