Agent Skills
PublicationPeer-review
Rebuttal Letter Strategist
AIPOCH-AI
In response to the reviewers' sharp Criticism, generate a "soft yet firm" counterattack strategy and response text that is neither servile nor overbearing while clearly explaining the situation.
39
1
FILES
rebuttal-letter-strategist/
skill.md
scripts
main.py
SKILL.md
Rebuttal Letter Strategist
"Soft but firm" rebuttal response generation.
Use Cases
- Major revision responses
- Rejection appeals
- Point-by-point rebuttals
Parameters
| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
criticism | str | Yes | - | Reviewer comment text to respond to |
response_type | str | No | "Partial" | Response type: "Accept", "Partial", or "Reject" |
evidence | str | No | - | Supporting data for the response |
Returns
- Professionally toned response
- Strategic positioning
- Evidence integration
Example
Transforms "We disagree" → "We respectfully maintain..."
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
No additional Python packages required.
Evaluation Criteria
Success Metrics
- Successfully executes main functionality
- Output meets quality standards
- Handles edge cases gracefully
- Performance is acceptable
Test Cases
- Basic Functionality: Standard input → Expected output
- Edge Case: Invalid input → Graceful error handling
- 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