Skills iacuc-protocol-drafter

Draft IACUC protocol applications with focus on the 3Rs principles justification

install

source · Clone the upstream repo

git clone https://github.com/openclaw/skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/aipoch-ai/iacuc-protocol-drafter" ~/.claude/skills/clawdbot-skills-iacuc-protocol-drafter && rm -rf "$T"

manifest: skills/aipoch-ai/iacuc-protocol-drafter/SKILL.md

source content

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

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