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Lay Summary Gen
ASecurityConverts complex medical abstracts into plain language summaries for
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- Added September 12, 2026
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[](https://www.skillsdirectory.com/skills/whyzsm-lay-summary-gen)---
name: lay-summary-gen
description: Converts complex medical abstracts into plain language summaries for
patients, caregivers, and the general public. Ensures readability while maintaining
scientific accuracy.
---
# Lay Summary Gen
Generates plain-language summaries of medical research for non-expert audiences.
## Features
- Complex to simple language conversion
- Jargon elimination
- Reading level optimization (Grade 6-8)
- Key takeaways extraction
- EU CTR compliance support
## Input Parameters
| Parameter | Type | Required | Description |
|-----------|------|----------|-------------|
| `abstract` | str | Yes | Original medical abstract |
| `target_audience` | str | No | "patients", "public", "media" |
| `max_words` | int | No | Maximum word count (default: 250) |
## Output Format
```json
{
"lay_summary": "string",
"reading_level": "string",
"key_takeaways": ["string"],
"word_count": "int",
"jargon_replaced": [{"term": "plain"}]
}
```
## 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
Files in this skill
- SKILL.md
- agents/openai.yaml
- references/guidelines.md
- scripts/main.py
- source.json
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