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comparison-table-gen

Auto-generates comparison tables for concepts, drugs, or study results in Markdown format.

skill-install — Terminal

Install via CLI (Recommended)

clawhub install openclaw/skills/skills/aipoch-ai/comparison-table-gen

Download Source Code (.zip)

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

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

Read Full Documentation on GitHub

Metadata

Author@aipoch-ai

Stars4473

Updated2026-05-01

View Author Profile

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Add to Configuration

Paste this into your clawhub.json to enable this plugin.

{
  "plugins": {
    "official-aipoch-ai-comparison-table-gen": {
      "enabled": true,
      "auto_update": true
    }
  }
}

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