— name: concept-explainer description: Uses analogies to explain complex medical concepts in accessible terms. version: 1.0.0 category: Info tags: – education – analogies – medical-concepts – explanation author: AIPOCH license: MIT status: Draft risk_level: Medium skill_type: Tool/Script owner: AIPOCH reviewer: '' last_updated: '2026-02-06' — # 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 “`bash # 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 “`json { "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 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