response-tone-polisher

name	response-tone-polisher
description	Polish reviewer-response letters by softening defensive language, preserving factual meaning, and keeping responses professional, concise, and publication-appropriate.
license	MIT
author	AIPOCH

Source: https://github.com/aipoch/medical-research-skills

Response Tone Polisher

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

Quick Check

Use this command to verify that the packaged script entry point can be parsed before deeper execution.

python -m py_compile scripts/main.py

Audit-Ready Commands

Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths.

python -m py_compile scripts/main.py
python scripts/main.py --help

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

• Tone Analysis: Identifies defensive, confrontational, or overly direct language
• Polite Transformation: Converts harsh statements into courteous academic prose
• Position Preservation: Maintains the author's scientific stance while improving delivery
• Context Awareness: Adapts based on response type (acceptance, partial acceptance, respectful decline)
• Academic Expression Library: Built-in collection of polished academic phrasings

When to Use

• Use this skill when the task needs Polishes response letters by transforming defensive or harsh language.
• Use this skill for academic writing tasks that require explicit assumptions, bounded scope, and a reproducible output format.
• Use this skill when you need a documented fallback path for missing inputs, execution errors, or partial evidence.

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

• "No" / "We won't" → "We respectfully decline to..."
• "We can't" → "We are unable to..."

2. Defensive Statements

• "But we already..." → "We have now clarified..."
• "This is not correct" → "We respectfully note that..."

3. Blame Shifting

• "The reviewer misunderstood" → "We apologize for the lack of clarity; we have revised..."
• "This is standard" → "This approach aligns with established conventions..."

4. Emotional Language

• "Unfortunately" (overused) → [removed or softened]
• "Obviously" → [removed]
• "Clearly" → [removed or context-dependent]

Polite Academic Expressions

Acknowledging Reviewers

• "We thank the reviewer for this insightful observation."
• "We appreciate the reviewer's careful attention to this detail."
• "We are grateful for this constructive feedback."
• "This is an excellent point."

Expressing Disagreement Diplomatically

• "We respectfully offer an alternative interpretation..."
• "Upon careful reconsideration, we believe..."
• "While we appreciate this perspective, we note that..."
• "We respectfully maintain our position that..."

Explaining Limitations

• "We acknowledge this limitation and have addressed it by..."
• "This constraint reflects the trade-off between..."
• "We have added appropriate caveats regarding this limitation."

Describing Changes

• "We have revised the manuscript to clarify..."
• "We have expanded the relevant section to include..."
• "We have incorporated this suggestion by..."

Workflow

1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work.
2. Validate that the request matches the documented scope and stop early if the task would require unsupported assumptions.
3. Use the packaged script path or the documented reasoning path with only the inputs that are actually available.
4. Return a structured result that separates assumptions, deliverables, risks, and unresolved items.
5. If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion.

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

• references/polite_expressions.json - Curated library of academic polite expressions
• references/tone_patterns.md - Common defensive patterns and their transformations
• references/examples/ - Before/after polishing examples

Limitations

• Does not verify scientific accuracy of responses
• Requires human review for complex nuanced disagreements
• May over-soften; authors should verify position is still clear
• Best for English-language responses

Quality Checklist

After polishing, verify:

• Original scientific position is preserved
• No confrontational language remains
• Professional tone throughout
• Clear acknowledgment of reviewer's effort
• Specific changes are still referenced
• Response directly addresses the comment

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

Output Requirements

Every final response should make these items explicit when they are relevant:

• Objective or requested deliverable
• Inputs used and assumptions introduced
• Workflow or decision path
• Core result, recommendation, or artifact
• Constraints, risks, caveats, or validation needs
• Unresolved items and next-step checks

Error Handling

• If required inputs are missing, state exactly which fields are missing and request only the minimum additional information.
• If the task goes outside the documented scope, stop instead of guessing or silently widening the assignment.
• If scripts/main.py fails, report the failure point, summarize what still can be completed safely, and provide a manual fallback.
• Do not fabricate files, citations, data, search results, or execution outcomes.

Input Validation

This skill accepts requests that match the documented purpose of response-tone-polisher and include enough context to complete the workflow safely.

Do not continue the workflow when the request is out of scope, missing a critical input, or would require unsupported assumptions. Instead respond:

response-tone-polisher only handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill.

Response Template

Use the following fixed structure for non-trivial requests:

1. Objective
2. Inputs Received
3. Assumptions
4. Workflow
5. Deliverable
6. Risks and Limits
7. Next Checks

If the request is simple, you may compress the structure, but still keep assumptions and limits explicit when they affect correctness.

When Not to Use

• Do not proceed when required input files, identifiers, parameters, or context are missing — ask the user to provide them first.
• Do not assume capabilities beyond this skill's declared scope when the user requests external operations or inferences.
• Do not proceed without user confirmation when overwriting existing results, executing high-cost batch operations, or expanding task scope.

Required Inputs

Field	Required	Format/Source	Example	If Missing
User task description	Yes	Text	Research question, writing goal, analysis objective	Stop and ask user to provide
Primary input material	Depends on task	Text, file path, ID, table, or literature	PMID, PDF, CSV, DOCX, keywords, etc.	Specify which material type is missing
Output preference	No	Text	Language, format, target journal, template	Use skill default format

Output Contract

• Primary output: Structured result or target file aligned with this skill's objective.
• Optional output: Intermediate check notes, issue list, supplementary suggestions, or generated file paths.
• Format requirement: Unless the user specifies otherwise, prefer stable, reviewable Markdown or JSON; if the skill's bundled script requires a fixed format, use that format.
• If partially complete: Must explicitly mark as PARTIAL and state which steps are completed and which remain.

Failure Handling

• Missing critical input: Explicitly state which fields, files, or identifiers are missing and pause.
• Script, template, or resource execution failure: Report the failing step, likely cause, and recovery suggestions — do not silently degrade.
• Partial completion only: Return the verified portion first, then list remaining blockers and suggested next steps.

User Checkpoints

• Before executing batch processing, overwriting files, long-running searches, or multi-stage generation, confirm scope and output format with the user.
• Before proceeding when a key judgment is ambiguous, evidence is insufficient, or the workflow is entering the next stage, confirm with the user.

Quick Validation

• Check that key scripts, templates, or reference file paths this skill depends on exist.
• Check that the final output contains the core fields, sections, or files specified for this task.
• Check that results clearly mark assumptions, limitations, and incomplete items.