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Skills q-and-a-prep-partner

Predict challenging questions for presentations and prepare responses

install
source · Clone the upstream repo
git clone https://github.com/openclaw/skills
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/aipoch-ai/qa-prep-partner" ~/.claude/skills/openclaw-skills-q-and-a-prep-partner && rm -rf "$T"
OpenClaw · Install into ~/.openclaw/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.openclaw/skills && cp -r "$T/skills/aipoch-ai/qa-prep-partner" ~/.openclaw/skills/openclaw-skills-q-and-a-prep-partner && rm -rf "$T"
manifest: skills/aipoch-ai/qa-prep-partner/SKILL.md
tags
#question-generation#presentation-prep#response-frameworks#content-creation#research-analysis
source content

Q&A Prep Partner

Predict challenging questions for presentations and prepare structured responses.

Usage

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

  1. Methodology questions
  2. Statistical questions
  3. Interpretation questions
  4. Limitation questions
  5. Future work questions
  6. Comparison questions

Output

  • Predicted questions
  • Suggested response frameworks
  • Key points to address

Risk Assessment

Risk IndicatorAssessmentLevel
Code ExecutionPython/R scripts executed locallyMedium
Network AccessNo external API callsLow
File System AccessRead input files, write output filesMedium
Instruction TamperingStandard prompt guidelinesLow
Data ExposureOutput files saved to workspaceLow

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

  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