Peer Review Response Drafter

Assist in drafting professional peer review response letters. Trigger when user mentions "reviewer comments", "response letter", "peer review", "revise and r...

Security Scan

The skill's code, instructions, and requirements align with its stated purpose (drafting peer-review response letters); there are no unexplained permissions, external endpoints, or credential requests.

Purpose & Capability

Name and description (drafting peer-review responses) match the included materials (SKILL.md, templates) and the bundled script (scripts/main.py). No unrelated credentials, binaries, or config paths are requested.

Instruction Scope

SKILL.md describes parsing reviewer comments, drafting responses, and tone adjustments; the instructions and parameters (interactive mode, input-file, tone, format) stay within that scope. The skill expects input text/files from the user but does not instruct reading unrelated system files or accessing external services.

Install Mechanism

No install spec is provided (instruction-only with an included helper script). This is the lowest-risk pattern; the bundled Python script and small requirements.txt are proportional to the task. There are no downloads from remote URLs or archive extraction steps.

Credentials

The skill requests no environment variables, credentials, or config paths. The requirement list is empty and the code shown contains no network calls or credential access. Input comes from user-provided files/strings, which is appropriate for this purpose.

Persistence & Privilege

Flags are default (always:false, disable-model-invocation:false). The skill does not request permanent presence or escalate privileges, nor does it modify other skills or system-wide settings.

Assessment

This skill appears coherent and limited to drafting responses. Before installing or using it, consider: 1) privacy—you will likely paste or upload unpublished manuscript text and reviewer comments, so avoid supplying sensitive or embargoed data to third parties or shared environments; 2) execution context—the included Python script runs locally in the agent environment, so review the script if you run it outside a sandbox; 3) trust—if you intend to run this in a managed/cloud agent, confirm the operator's data handling policy. If you want extra assurance, inspect the full scripts/main.py file (no network calls or credential access were found in the provided content).

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:

Tone	Use Case	Example Phrasing
Diplomatic	General revisions	"We thank..." / "We appreciate..." / "We have revised..."
Assertive	Defending methodology	"We respectfully note..." / "Our approach is justified because..."
Grateful	Major 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

Parameter	Type	Required	Default	Description
--interactive	flag	No	-	Interactive mode: Guided wizard with prompts (uses input()). Recommended for first-time users or complex responses
--input-file	str	No	-	Path to reviewer comments file (automation mode)
--output	str	No	-	Output file path for response letter
--tone	str	No	"diplomatic"	Response tone: "diplomatic", "formal", or "assertive"
--format	str	No	"markdown"	Output format: "markdown", "plain_text", or "latex"
--include-diff	bool	No	true	Whether 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 Indicator	Assessment	Level
Code Execution	Python/R scripts executed locally	Medium
Network Access	No external API calls	Low
File System Access	Read input files, write output files	Medium
Instruction Tampering	Standard prompt guidelines	Low
Data Exposure	Output files saved to workspace	Low

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