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// Skill profile

IACUC Protocol Drafter

name: iacuc-protocol-drafter

by aipoch-ai · published 2026-04-01

数据处理API集成
Total installs
0
Stars
★ 0
Last updated
2026-04
// Install command
$ claw add gh:aipoch-ai/aipoch-ai-iacuc-protocol-drafter
View on GitHub
// Full documentation

---

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

    # 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)

    {
      "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

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