Q&A Prep Partner

Predict challenging questions for presentations and prepare responses

AIpoch@aipoch-ai

Install

openclaw skills install @aipoch-ai/qa-prep-partner

Q&A Prep Partner

Predict challenging questions for presentations and prepare structured responses.

Usage

bash

python scripts/main.py --abstract abstract.txt --field oncology
python scripts/main.py --topic "CRISPR therapy" --audience experts

Parameters

--abstract: Abstract text or file
--topic: Research topic
--field: Research field
--audience: Audience type (general/experts/peers)
--n-questions: Number of questions to generate (default: 10)

Question Types

Methodology questions
Statistical questions
Interpretation questions
Limitation questions
Future work questions
Comparison questions

Output

Predicted questions
Suggested response frameworks
Key points to address

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