biomarker-analysis
Ralph-Inspired Adaptive Learning Framework for iterative, quality-gated biomarker report analysis
npx skills add majiayu000/claude-skill-registry --skill biomarker-analysis --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Biomarker Analysis Pipeline (RALF) Ralph-Inspired Adaptive Learning Framework for iterative, quality-gated biomarker report analysis. ## Overview This skill runs comprehensive biomarker analysis using the RALF architecture: - **Iterative Processing**: Each category processed independently with fresh context - **KB Enrichment**: Per-category VectorShift KB queries inform analysis - **Dual Validation Gates**: Structural completeness + clinical accuracy - **Retry with Feedback**: Failed validations trigger specific revision prompts - **State Persistence**: Resume from any point, track learnings ## Usage ### Basic Analysis ```bash # Run from Python from lib.biomarker_analysis.execution import run_analysis biomarkers = [ {"name": "A1c", "value": "5.8", "unit": "%", "ref_range": "<5.7"}, {"name": "Fasting Glucose", "value": "98", "unit": "mg/dL", "ref_range": "70-99"}, # ... more biomarkers ] prd = run_analysis( biomarkers=biomarkers, user_preference_level=3, # 1-5 scale patient_goals="Optimize metabolic health and longevity", ) ``` ### Resume Interrupted Analysis ```bash from lib.biomarker_analysis.execution import resume_analysis prd = resume_analysis(output_dir=".biomarker-analysis"
- Overview
- Usage
- Basic Analysis
- Resume Interrupted Analysis
- Generate Visual Report
- Architecture
- Categories
- Core Categories (Always Present)
- Dynamic Modules (Auto-detected)
- User Preference Levels
- Clinical Conventions
- Output Files
- Validation Gates
- Gate 1: Structural Completeness
Run from Python from lib.biomarker_analysis.execution import run_analysis biomarkers = [ prd = run_analysis( from lib.biomarker_analysis.execution import resume_analysis prd = resume_analysis(output_dir=".biomarker-analysis") from lib.biomarker_analysis.synthesis import synthesize_report from lib.biomarker_analysis.visual import generate_visual_report from lib.biomarker_analysis.state import StateManager state = StateManager()
What does the biomarker-analysis skill do?
Ralph-Inspired Adaptive Learning Framework for iterative, quality-gated biomarker report analysis
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill biomarker-analysis --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.