Two Sample Mr Research Planner 3
Skill by aipoch-ai
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/aipoch-ai/two-sample-mr-research-planner-3name: two-sample-mr-research-planner description: Generates complete two-sample Mendelian randomization (MR) research designs from a user-provided research direction. Use when users want to design, plan, or build a study using two-sample MR to test causal relationships. Triggers: "design a two-sample MR study", "build a publishable MR paper", "test whether this biomarker causally affects this disease", "generate Lite/Standard/Advanced MR plans", "screen multiple exposures with MR", "bidirectional MR design", "causal inference using GWAS summary statistics", or "I want to study X and Y using MR". Always outputs four workload configurations (Lite / Standard / Advanced / Publication+) with a recommended primary plan, step-by-step workflow, figure plan, validation strategy, minimal executable version, and publication upgrade path. license: MIT skill-author: AIPOCH
Two-Sample Mendelian Randomization Research Planner
Generates a complete two-sample MR study design from a user-provided research direction. Always outputs four workload configurations and a recommended primary plan.
Supported Study Styles
| Style | Description | Example |
|---|---|---|
| A. Single Exposure → Single Outcome | One biomarker or trait to one disease | Serum uric acid → gout; vitamin D → osteoporosis |
| B. Multi-Exposure Screening | Panel of exposures to one outcome | Dietary factors → endometriosis; cytokine panel → RA |
| C. Bidirectional MR | Reciprocal causal testing | Inflammation ↔ depression; BMI ↔ osteoarthritis |
| D. Lifestyle / Diet / Behavioral | Self-reported behavioral exposures | Coffee intake → hypertension; sleep duration → stroke |
| E. Biomarker / Molecular Trait | Circulating proteins, metabolites | Cytokines → autoimmune disease; plasma proteins → Alzheimer's |
| F. Publication-Oriented | Comprehensive sensitivity-rich design | Full estimator suite with complete figure set |
Minimum User Input
- One exposure (or exposure set) + one outcome
- If limited detail is provided, infer a reasonable default design and state all assumptions explicitly
Step-by-Step Execution
Step 1: Infer Study Type
Identify:
- Exposure(s) and outcome
- Exposure class (dietary, biomarker, metabolite, behavioral, disease trait, molecular)
- User goal: screening, bidirectional, causal verification, or publication strength
- Whether MVMR or colocalization is justified
- Time or resource constraints stated by the user
Step 2: Output Four Configurations
Always generate all four. For each configuration describe: goal, required data, major modules, expected workload, figure set, strengths, and weaknesses.
Metadata
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{
"plugins": {
"official-aipoch-ai-two-sample-mr-research-planner-3": {
"enabled": true,
"auto_update": true
}
}
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