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Q&A Prep Partner

面向学术或专业演示的问答准备工具。用户提供摘要或主题、领域、受众及问题数量后，本地脚本生成预测问题、建议应答框架和需覆盖的关键点，不调用外部 API。

aipoch-ai
aipoch-ai
热度 36.18
最近更新 2026-05-11 17:38:29
分类：知识依赖：代码执行 / 读写文件应用场景：研究

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add qa-prep-partner
Metadata
name
q-and-a-prep-partner
description
Predict challenging questions for presentations and prepare responses
version
1.0.0
category
Present
tags
[]
author
AIPOCH
license
MIT
status
Draft
risk_level
Medium
skill_type
Tool/Script
owner
AIPOCH
reviewer
last_updated
2026-02-06

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 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

  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