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Two Sample Mr Research Planner 3

Skill by aipoch-ai

skill-install — Terminal

Install via CLI (Recommended)

clawhub install openclaw/skills/skills/aipoch-ai/two-sample-mr-research-planner-3
Or

name: 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

StyleDescriptionExample
A. Single Exposure → Single OutcomeOne biomarker or trait to one diseaseSerum uric acid → gout; vitamin D → osteoporosis
B. Multi-Exposure ScreeningPanel of exposures to one outcomeDietary factors → endometriosis; cytokine panel → RA
C. Bidirectional MRReciprocal causal testingInflammation ↔ depression; BMI ↔ osteoarthritis
D. Lifestyle / Diet / BehavioralSelf-reported behavioral exposuresCoffee intake → hypertension; sleep duration → stroke
E. Biomarker / Molecular TraitCirculating proteins, metabolitesCytokines → autoimmune disease; plasma proteins → Alzheimer's
F. Publication-OrientedComprehensive sensitivity-rich designFull 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

Author@aipoch-ai
Stars4473
Views0
Updated2026-05-01
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Add to Configuration

Paste this into your clawhub.json to enable this plugin.

{
  "plugins": {
    "official-aipoch-ai-two-sample-mr-research-planner-3": {
      "enabled": true,
      "auto_update": true
    }
  }
}
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