Skill: aipoch-ai/iacuc-protocol-drafter (version 0.1.0)

--- name: iacuc-protocol-drafter description: Draft IACUC protocol applications with focus on the 3Rs principles justification version: 1.0.0 category: Pharma tags: [] author: AIPOCH license: MIT status: Draft risk_level: Medium skill_type: Tool/Script owner: AIPOCH reviewer: '' last_updated: '2026-02-06' --- # IACUC Protocol Drafter **ID**: 105 **Name**: IACUC Protocol Drafter **Description**: Draft Institutional Animal Care and Use Committee (IACUC) protocol applications, especially the justification section for the "3Rs principles" (Replacement, Reduction, Refinement). ## Requirements - Python 3.8+ - No additional dependencies (uses standard library) ## Usage ```bash # Generate local file python skills/iacuc-protocol-drafter/scripts/main.py --input protocol_input.json --output iacuc_protocol.txt # Use stdin/stdout cat protocol_input.json | python skills/iacuc-protocol-drafter/scripts/main.py ``` ## Parameters | Parameter | Type | Default | Required | Description | |-----------|------|---------|----------|-------------| | `--input`, `-i` | string | - | Yes | Path to input JSON file with protocol details | | `--output`, `-o` | string | stdout | No | Output file path for generated protocol | | `--template` | string | standard | No | Template type (standard, minimal, detailed) | | `--format` | string | text | No | Output format (text, markdown, docx) | ## Input Format (JSON) ```json { "title": "Experiment Title", "principal_investigator": "Principal Investigator Name", "institution": "Research Institution Name", "species": "Experimental Animal Species", "number_of_animals": 50, "procedure_description": "Brief description of experimental procedures", "pain_category": "B", "justification": { "replacement": { "alternatives_considered": ["In vitro experiments", "Computer simulation"], "why_animals_needed": "Reasons why animals must be used" }, "reduction": { "sample_size_calculation": "Sample size calculation method and rationale", "minimization_strategies": "Strategies to minimize animal numbers" }, "refinement": { "pain_management": "Pain management measures", "housing_enrichment": "Housing environment optimization", "humane_endpoints": "Humane endpoint setting" } } } ``` ## Output Generate IACUC-standard application text, including a complete 3Rs principles justification section. ## Templates Built-in standard templates cover: - **Replacement**: Justification for why live animals must be used - **Reduction**: Explanation of statistical basis for sample size calculation - **Refinement**: Description of measures to reduce pain and stress ## Notes - Generated content should be used as a draft and adjusted according to actual conditions - It is recommended to consult your institution's IACUC office for specific format requirements - Ensure all animal experiments comply with local regulations and institutional policies ## 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


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)