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Comparison Table Gen

该 Skill 通过本地 Python 脚本接收逗号分隔的待比较条目与比较属性,生成 Markdown 与 HTML 格式的对比表格,并可用 --output 将结果写入 JSON 文件。材料声明不调用外部 API、无需额外 Python 依赖,面向药物、治疗方案、研究结果等内容的并排比较场景。

aipoch-ai
aipoch-ai
热度 84.43
最近更新 2026-05-17 15:17:33
分类:效率依赖:代码执行 / 读写文件应用场景:研究 / 报告撰写

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 comparison-table-gen
Metadata
name
comparison-table-gen
description
Auto-generates comparison tables for concepts, drugs, or study results in Markdown format.
version
1.0.0
category
Info
tags
["comparison","table","markdown","research"]
author
AIPOCH
license
MIT
status
Draft
risk_level
Medium
skill_type
Tool/Script
owner
AIPOCH
reviewer
last_updated
2026-02-06

Comparison Table Gen

Generates comparison tables for medical content.

Features

  • Side-by-side comparisons
  • Markdown table output
  • Drug comparison templates
  • Study result comparisons

Parameters

Parameter Type Default Required Description
--items, -i string - Yes Items to compare (comma-separated)
--attributes, -a string - Yes Comparison attributes (comma-separated)
--output, -o string - No Output JSON file path

Usage

# Compare two drugs
python scripts/main.py --items "Drug A,Drug B" --attributes "Mechanism,Dose,Side Effects"

# Save to file
python scripts/main.py --items "Surgery,Chemo,Radiation" --attributes "Cost,Efficacy" --output comparison.json

Input Format

  • items: Comma-separated list of items to compare (e.g., "Drug A,Drug B")
  • attributes: Comma-separated list of comparison attributes (e.g., "Mechanism,Dose")

Output Format

{
  "markdown_table": "string",
  "html_table": "string"
}

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