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concept-explainer

Uses analogies to explain complex medical concepts in accessible terms.

skill-install — Terminal

Install via CLI (Recommended)

clawhub install openclaw/skills/skills/aipoch-ai/concept-explainer

Download Source Code (.zip)

Concept Explainer

Explains medical concepts using everyday analogies.

Features

Analogy generation
Concept simplification
Multiple explanation levels
Visual description support

Parameters

Parameter	Type	Default	Required	Description
`--concept`, `-c`	string	-	Yes	Medical concept to explain
`--audience`, `-a`	string	patient	No	Target audience (child, patient, student)
`--list`, `-l`	flag	-	No	List all available concepts
`--output`, `-o`	string	-	No	Output JSON file path

Usage

# Explain thrombosis to a patient
python scripts/main.py --concept "thrombosis"

# Explain to a child
python scripts/main.py --concept "immune system" --audience child

# Explain to a medical student
python scripts/main.py --concept "antibiotic resistance" --audience student

# List all available concepts
python scripts/main.py --list

Output Format

{
  "explanation": "string",
  "analogy": "string",
  "key_points": ["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

Stars3917

Updated2026-04-08

View Author Profile

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Add to Configuration

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

{
  "plugins": {
    "official-aipoch-ai-concept-explainer": {
      "enabled": true,
      "auto_update": true
    }
  }
}

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