Agent skill
concept-explainer
Uses analogies to explain complex medical concepts in accessible terms.
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/other/concept-explainer
SKILL.md
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
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