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Bio Causal Genomics Genomic Sem

Category: Science & Research  ·  Sub-category: math-stats  ·  Last updated:
Fits structural equation models to GWAS summary statistics using GenomicSEM (Grotzinger 2019), including common-factor models, confirmatory factor models, ESEM, common-factor GWAS with Q_SNP heterogeneity, multivariate Wald tests, and stratified GenomicSEM partitioned heritability. Reconciles results against MTAG multi-trait analysis. Handles sample overlap via the LDSC sampling-covariance matrix, identifies and resolves Heywood cases, and verifies model fit with CFI / RMSEA. Use when modeling latent genetic architecture across correlated traits, running multivariate GWAS on a shared factor, distinguishing factor-mediated from trait-specific SNP effects, or comparing GenomicSEM common-factor results against MTAG when both depend on accurate sampling covariance.

What this skill does

Bio Causal Genomics Genomic Sem is a community-contributed Claude Code skill in the math-stats sub-category. It ships as a SKILL.md file that Claude Code auto-discovers under ~/.claude/skills/bio-causal-genomics-genomic-sem/ and loads when your prompt matches the skill's trigger.

When to invoke it: Use when modeling latent genetic architecture across correlated traits, running multivariate GWAS on a shared factor, distinguishing factor-mediated from trait-specific SNP effects, or comparing GenomicSEM common-factor results against MTAG when both depend on accurate sampling covariance.

Who uses this skill

The Bio Causal Genomics Genomic Sem Claude Code skill is built for researchers, data scientists, academics, and analysts working with complex data and scientific literature. It's part of ClaudSkills (also referred to as Claude Skills or Claude Code Skills) — the open community-curated registry of 93,000+ SKILL.md files for Anthropic's Claude Code agent and the wider Claude ecosystem (Claude API, Claude Agent SDK).

How to install

Free

Manual install (2 steps)

mkdir -p ~/.claude/skills/bio-causal-genomics-genomic-sem
curl -L https://claudskills.com/skills/bio-causal-genomics-genomic-sem/SKILL.md \
  -o ~/.claude/skills/bio-causal-genomics-genomic-sem/SKILL.md

Or just download SKILL.md directly and drop it into ~/.claude/skills/bio-causal-genomics-genomic-sem/. Claude Code auto-discovers it on next session.

Skills live at ~/.claude/skills/bio-causal-genomics-genomic-sem/SKILL.md on macOS/Linux, or %USERPROFILE%\.claude\skills\bio-causal-genomics-genomic-sem\SKILL.md on Windows. See the full install guide for step-by-step instructions.

Telegram

📱 Install from your phone or desktop Telegram

Open @claudskills_bot on Telegram, tap Open Desktop App, and the desktop app installs this skill for you. Or share the bot link with a colleague — they get the same one-tap install. Learn more →

Pro

One-click install via the desktop app

The ClaudSkills desktop app installs any skill directly into ~/.claude/skills/ with one click — no terminal required. Pro starts at $9/mo or $149 lifetime.

Pro

For the full experience including quality scoring and one-click install features for each skill — upgrade to Pro.

Frequently asked questions

How do I install the Bio Causal Genomics Genomic Sem Claude Code skill?
Install via the ClaudSkills desktop app (one click) or copy SKILL.md from the source repository to ~/.claude/skills/bio-causal-genomics-genomic-sem/SKILL.md and restart Claude Code. Both flows are detailed at claudskills.com/install/.
What does the Bio Causal Genomics Genomic Sem skill do?
Fits structural equation models to GWAS summary statistics using GenomicSEM (Grotzinger 2019), including common-factor models, confirmatory factor models, ESEM, common-factor GWAS with Q_SNP heterogeneity, multivariate Wald tests, and stratified GenomicSEM partitioned heritability. Reconciles results against MTAG multi-trait analysis. Handles sample overlap via the LDSC sampling-covariance matrix, identifies and resolves Heywood cases, and verifies model fit with CFI / RMSEA. Use when modeling latent genetic architecture across correlated traits, running multivariate GWAS on a shared factor, distinguishing factor-mediated from trait-specific SNP effects, or comparing GenomicSEM common-factor results against MTAG when both depend on accurate sampling covariance.
Is this skill free to install?
Yes. ClaudSkills is an open registry — every skill keeps its source repository's license, and manual install via copy is free. ClaudSkills Pro ($9/mo, $79/yr, or $149 one-time) adds one-click install via the desktop app and a multi-signal Quality Score.
When should I use the Bio Causal Genomics Genomic Sem skill?
Use Bio Causal Genomics Genomic Sem when your Claude Code task falls under the Science & Research category — specifically in the math stats area. Claude Code auto-discovers installed skills and invokes the right one based on the task description, so you can also ask Claude directly (e.g. "use Bio Causal Genomics Genomic Sem" or describe the task and let Claude pick). Browse related skills at /category/science/.
What is a Claude Code skill and how does the Bio Causal Genomics Genomic Sem skill fit in?
A Claude Code skill is a SKILL.md file that lives under ~/.claude/skills/<name>/ and tells the Claude Code CLI agent how to perform a specific task (instructions, prompts, allowed tools). Skills are auto-discovered at session start. Bio Causal Genomics Genomic Sem is one of 67,000+ skills indexed in the open ClaudSkills catalog, classified under the Science & Research category. Learn more at /learn/what-is-a-claude-skill/.

Attribution & license

Cite this skill

If you reference this skill in a blog post, paper, or documentation, you can cite it as:

APA
bg-szy. (2026). Bio Causal Genomics Genomic Sem [Claude Code skill]. ClaudSkills. https://claudskills.com/skills/bio-causal-genomics-genomic-sem/
BibTeX
@misc{bio-causal-genomics-genomic-sem-2026,
  author    = {bg-szy},
  title     = {Bio Causal Genomics Genomic Sem [Claude Code skill]},
  year      = {2026},
  publisher = {ClaudSkills},
  url       = {https://claudskills.com/skills/bio-causal-genomics-genomic-sem/}
}

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