Randomization Gen
RCT randomization table generator.
Use Cases
- Clinical trial design
- Animal study randomization
- Blocked randomization
- Stratified allocation
Parameters
| Parameter | Type | Required | Description |
|---|
| ----------- | ------ | ---------- | ------------- |
n_subjects | int | Yes | Total sample size |
n_groups | int | Yes | Number of arms/groups |
block_size | int | Yes | Block size (must be multiple of n_groups) |
--output | string | No | Output file path (default: randomization.txt) |
Returns
- Randomization sequence
- Block assignments
- Allocation concealment ready
Example
Input: n=120, 3 groups, block=6
Output: Sealed randomization list
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