Agent skill

tooluniverse-gwas-study-explorer

Compare GWAS studies, perform meta-analyses, and assess replication across cohorts. Integrates NHGRI-EBI GWAS Catalog and Open Targets Genetics to compare study designs, effect sizes, ancestry diversity, and heterogeneity statistics. Use when comparing GWAS studies for a trait, performing meta-analysis of genetic loci, assessing replication across cohorts, or exploring the genetic architecture of complex diseases.

BioTender-maxgithub.com/BioTender-maxGitHub ↗
claude-codeships scriptsNOASSERTION
Install
npx skills add BioTender-max/awesome-bio-agent-skills --skill tooluniverse-gwas-study-explorer --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 6
SKILL.md size: 11 KB
Bundled scripts: yes
Path: skills/openclaw/tooluniverse-gwas-study-explorer/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 135
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# GWAS Study Deep Dive & Meta-Analysis **Compare GWAS studies, perform meta-analyses, and assess replication across cohorts** --- ## Overview The GWAS Study Deep Dive & Meta-Analysis skill enables comprehensive comparison of genome-wide association studies (GWAS) for the same trait, meta-analysis of genetic loci across studies, and systematic assessment of replication and study quality. It integrates data from the NHGRI-EBI GWAS Catalog and Open Targets Genetics to provide a complete picture of the genetic architecture of complex traits. ### Key Capabilities 1. **Study Comparison**: Compare all GWAS studies for a trait, assessing sample sizes, ancestries, and platforms 2. **Meta-Analysis**: Aggregate effect sizes across studies and calculate heterogeneity statistics 3. **Replication Assessment**: Identify replicated vs novel findings across discovery and replication cohorts 4. **Quality Evaluation**: Assess statistical power, ancestry diversity, and data availability --- ## Use Cases ### 1. Comprehensive Trait Analysis **Scenario**: "I want to understand all available GWAS data for type 2 diabetes" **Workflow**: - Search for all T2D studies in GWAS Catalog - Filter by sample size a

What's inside
Steps it walks through
  1. Overview
  2. Key Capabilities
  3. Use Cases
  4. 1. Comprehensive Trait Analysis
  5. 2. Locus-Specific Meta-Analysis
  6. 3. Replication Analysis
  7. 4. Multi-Ancestry Comparison
  8. Statistical Methods
  9. Meta-Analysis Approach
  10. Sources of Heterogeneity
  11. Study Quality Assessment
  12. Quality Metrics
  13. Quality Tiers
  14. Best Practices
Ships with 5 files
  • .env.template
  • QUICK_START.md
  • README.md
  • python_implementation.py
  • test_skill_comprehensive.py
More from awesome-bio-agent-skills
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About this skill
What does the tooluniverse-gwas-study-explorer skill do?

Compare GWAS studies, perform meta-analyses, and assess replication across cohorts. Integrates NHGRI-EBI GWAS Catalog and Open Targets Genetics to compare study designs, effect sizes, ancestry diversity, and heterogeneity statistics. Use when comparing GWAS studies for a trait, performing meta-analysis of genetic loci, assessing replication across cohorts, or exploring the genetic architecture of complex diseases.

How do I install it?

Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill tooluniverse-gwas-study-explorer --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 BioTender-max/awesome-bio-agent-skills, a repository with 135 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.

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