Response Tone Polisher

Response Tone Polisher

Polishes response letters to peer reviewers by softening harsh or defensive language while preserving the author's position and scientific integrity.

Overview

This skill analyzes author draft responses to reviewer comments and transforms confrontational or defensive phrasing into professional, diplomatic academic language. It helps researchers maintain positive relationships with reviewers while standing firm on scientifically justified positions.

Key Features

When to Use

Usage Examples

Basic Usage

Input:
Reviewer: The sample size is too small for meaningful conclusions.
Draft Response: I disagree. Our sample size is standard in this field.
Output:
We appreciate the reviewer's concern regarding sample size. While we acknowledge 
that larger samples provide greater statistical power, our sample size is consistent 
with established conventions in this field and meets the requirements for adequate 
power analysis (as detailed in the Methods section).

Defensive Language Transformation

| Original (Defensive) | Polished (Professional) | |---------------------|------------------------| | "I will not change this." | "We have carefully considered this suggestion and respectfully maintain our original approach because..." | | "The reviewer is wrong." | "We respectfully offer a different interpretation..." | | "This is unnecessary." | "We appreciate this suggestion; however, we believe the current presentation adequately addresses this point." | | "We already explained this." | "We have expanded our explanation to enhance clarity (Page X, Lines Y-Z)." | | "That's not our fault." | "We acknowledge this limitation and have added appropriate caveats to the Discussion." |

Input Parameters

| Parameter | Type | Required | Description | |-----------|------|----------|-------------| | reviewer_comment | str | Yes | The reviewer's original comment or criticism | | draft_response | str | Yes | Author's initial draft response (may contain harsh/defensive language) | | response_type | str | No | One of: accept, partial, decline (default: auto-detect) | | polish_level | str | No | light, moderate, heavy (default: moderate) | | preserve_meaning | bool | No | Ensure scientific position is preserved (default: true) |

Output Format

{
  "polished_response": "string",
  "original_tone_score": "float (0-1, higher = more defensive)",
  "improvements": [
    {
      "original_phrase": "string",
      "polished_phrase": "string",
      "issue_type": "string"
    }
  ],
  "suggestions": ["string"],
  "politeness_score": "float (0-1)"
}

Tone Patterns Detected

The skill identifies and transforms:

1. Direct Refusals

2. Defensive Statements

3. Blame Shifting

4. Emotional Language

Polite Academic Expressions

Acknowledging Reviewers

Expressing Disagreement Diplomatically

Explaining Limitations

Describing Changes

Workflow

1. Input Analysis: Parse reviewer comment and draft response 2. Tone Assessment: Score defensiveness and identify problematic phrases 3. Pattern Matching: Find harsh expressions in the transformation library 4. Reconstruction: Rewrite maintaining scientific accuracy 5. Quality Check: Verify politeness and clarity

Command Line Usage

# Interactive mode
python scripts/main.py --interactive
File-based
python scripts/main.py \
  --reviewer-comment "comment.txt" \
  --draft-response "draft.txt" \
  --output "polished.txt"
Direct input
python scripts/main.py \
  --reviewer "The data is insufficient." \
  --draft "You are wrong. We have enough data." \
  --polish-level heavy

Python API

from scripts.main import TonePolisher
polisher = TonePolisher()
result = polisher.polish(
    reviewer_comment="The methodology is flawed.",
    draft_response="No it's not. We did it right.",
    response_type="decline",
    polish_level="moderate"
)
print(result["polished_response"])

References

Limitations

Quality Checklist

After polishing, verify:

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

Prerequisites

# Python dependencies
pip install -r requirements.txt

Evaluation Criteria

Success Metrics

Test Cases

Lifecycle Status

# Python dependencies
pip install -r requirements.txt