Skill: aipoch-ai/lay-summary-gen-1 (version 1.0.0)

--- name: lay-summary-gen description: Converts complex medical abstracts into plain language summaries for. license: MIT skill-author: AIPOCH --- # Lay Summary Gen Generates plain-language summaries of medical research for non-expert audiences. ## When to Use - Use this skill when the task needs Converts complex medical abstracts into plain language summaries for. - Use this skill for academic writing tasks that require explicit assumptions, bounded scope, and a reproducible output format. - Use this skill when you need a documented fallback path for missing inputs, execution errors, or partial evidence. ## Key Features See `## Features` above for related details. - Scope-focused workflow aligned to: Converts complex medical abstracts into plain language summaries for. - Packaged executable path(s): `scripts/main.py`. - Reference material available in `references/` for task-specific guidance. - Structured execution path designed to keep outputs consistent and reviewable. ## Dependencies See `## Prerequisites` above for related details. - `Python`: `3.10+`. Repository baseline for current packaged skills. - `Third-party packages`: `not explicitly version-pinned in this skill package`. Add pinned versions if this skill needs stricter environment control. ## Example Usage ```bash cd "20260318/scientific-skills/Academic Writing/lay-summary-gen" python -m py_compile scripts/main.py python scripts/main.py --help ``` Example run plan: 1. Confirm the user input, output path, and any required config values. 2. Edit the in-file `CONFIG` block or documented parameters if the script uses fixed settings. 3. Run `python scripts/main.py` with the validated inputs. 4. Review the generated output and return the final artifact with any assumptions called out. ## Implementation Details See `## Workflow` above for related details. - Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable. - Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script. - Primary implementation surface: `scripts/main.py`. - Reference guidance: `references/` contains supporting rules, prompts, or checklists. - Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints. - Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects. ## Quick Check Use this command to verify that the packaged script entry point can be parsed before deeper execution. ```bash python -m py_compile scripts/main.py ``` ## Audit-Ready Commands Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths. ```bash python -m py_compile scripts/main.py python scripts/main.py demo ``` ## Workflow 1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work. 2. Validate that the request matches the documented scope and stop early if the task would require unsupported assumptions. 3. Use the packaged script path or the documented reasoning path with only the inputs that are actually available. 4. Return a structured result that separates assumptions, deliverables, risks, and unresolved items. 5. If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion. ## 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 ## Output Requirements Every final response should make these items explicit when they are relevant: - Objective or requested deliverable - Inputs used and assumptions introduced - Workflow or decision path - Core result, recommendation, or artifact - Constraints, risks, caveats, or validation needs - Unresolved items and next-step checks ## Error Handling - If required inputs are missing, state exactly which fields are missing and request only the minimum additional information. - If the task goes outside the documented scope, stop instead of guessing or silently widening the assignment. - If `scripts/main.py` fails, report the failure point, summarize what still can be completed safely, and provide a manual fallback. - Do not fabricate files, citations, data, search results, or execution outcomes. ## Input Validation This skill accepts requests that match the documented purpose of `lay-summary-gen` and include enough context to complete the workflow safely. Do not continue the workflow when the request is out of scope, missing a critical input, or would require unsupported assumptions. Instead respond: > `lay-summary-gen` only handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill. ## Response Template Use the following fixed structure for non-trivial requests: 1. Objective 2. Inputs Received 3. Assumptions 4. Workflow 5. Deliverable 6. Risks and Limits 7. Next Checks If the request is simple, you may compress the structure, but still keep assumptions and limits explicit when they affect correctness.

path: references/guidelines.md
content: # Lay Summary Gen - References

## Plain Language Standards
- EU Clinical Trials Regulation (CTR) - Lay Summary Requirements
- NIH Plain Language Guidelines
- Plain Language Action and Information Network (PLAIN)

## Health Literacy
- Health Literacy Universal Precautions Toolkit
- AHRQ Health Literacy Resources


path: scripts/main.py
content: #!/usr/bin/env python3
"""Lay Summary Generator - Plain language medical summary creator."""

import json
import re
from typing import Dict, List

class LaySummaryGenerator:
    """Creates plain-language summaries of medical research."""
    
    JARGON = {
        "randomized controlled trial": "study where participants were randomly assigned to treatments",
        "placebo": "inactive substance for comparison",
        "double-blind": "neither participants nor researchers knew who got which treatment",
        "efficacy": "how well the treatment works",
        "adverse events": "side effects",
        "placebo-controlled": "compared against inactive treatment",
        "multicenter": "conducted at multiple hospitals",
        "inclusion criteria": "requirements to participate",
        "exclusion criteria": "factors preventing participation",
        "primary endpoint": "main result being measured",
        "secondary endpoint": "additional results measured"
    }
    
    def generate(self, abstract: str, target: str = "public", max_words: int = 250) -> Dict:
        """Generate lay summary."""
        
        # Replace jargon
        summary = abstract
        jargon_replaced = []
        
        for term, plain in self.JARGON.items():
            if term.lower() in summary.lower():
                summary = re.sub(term, plain, summary, flags=re.IGNORECASE)
                jargon_replaced.append({"term": term, "plain": plain})
        
        # Simplify structure
        summary = self._simplify_sentences(summary)
        
        # Extract key points
        takeaways = self._extract_takeaways(summary)
        
        # Count words
        word_count = len(summary.split())
        
        # Calculate reading level
        reading_level = self._estimate_reading_level(summary)
        
        return {
            "lay_summary": summary[:max_words * 6],  # Rough character limit
            "original_word_count": len(abstract.split()),
            "summary_word_count": word_count,
            "reading_level": reading_level,
            "target_audience": target,
            "key_takeaways": takeaways,
            "jargon_replaced": jargon_replaced
        }
    
    def _simplify_sentences(self, text: str) -> str:
        """Simplify sentence structures."""
        # Break long sentences
        text = text.replace(";", ".")
        text = text.replace(" furthermore", ". Also")
        text = text.replace(" however", ". But")
        text = text.replace(" moreover", ". Also")
        
        return text
    
    def _extract_takeaways(self, text: str) -> List[str]:
        """Extract key points."""
        sentences = text.split(".")
        takeaways = []
        
        for sent in sentences:
            sent = sent.strip()
            if any(word in sent.lower() for word in ["found", "showed", "result", "conclusion", "suggests"]):
                if len(sent) > 20:
                    takeaways.append(sent)
        
        return takeaways[:3]
    
    def _estimate_reading_level(self, text: str) -> str:
        """Estimate reading grade level."""
        words = text.split()
        avg_word_length = sum(len(w) for w in words) / len(words) if words else 0
        
        if avg_word_length < 4.5:
            return "Grade 6-8 (Easy)"
        elif avg_word_length < 5.5:
            return "Grade 9-10 (Average)"
        return "Grade 11+ (Advanced)"

def main():
    import sys
    gen = LaySummaryGenerator()
    
    abstract = sys.argv[1] if len(sys.argv) > 1 else "This RCT evaluated efficacy of Drug X in N=200 patients."
    result = gen.generate(abstract)
    print(json.dumps(result, indent=2))

if __name__ == "__main__":
    main()


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