GWAS and population genetics tool. Processes PLINK (.bed/.bim/.fam), VCF, and BGEN; runs QC (MAF, HWE, missingness), IBD estimation, PCA, and linear/logistic regression GWAS. Outputs Manhattan-ready summary stats. Use regenie or SAIGE for biobanks (>100k samples) needing mixed models.
SkillScore82Maintained
Automatic
◆Research pack
Last verified 9/3/2026
What it changes
Passed 16 of 19 test cases with the skill, and 15 of 19 without it.
Without skill77%
With skill82%
Checks it passed that the model alone missed
Uses `--logistic firth` for Firth-penalized logistic regression.
Recodes 1/2 phenotype designations to 0/1 binary values using `--1`.
Standardizes covariate variances across quantitative trait predictors.
3 fixed15 passed either way2 still failing2 broke
Pass-rate lift
+5 pp
Tokens
+151%
Agent turns
0%
Response time
-22%
Model gemini-3.6-flashMeasured 9/3/2026v2 · latest is v318 of 22 cases passed with skill · 19 comparable
Tokens, turns and time describe resource use, not a dollar price.
This benchmark measured an earlier version of the skill.
See it in action
One of 3 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 3
Prompt
We are analyzing a quantitative trait in 5,000 samples with 500,000 variants in PLINK binary format (`cohort_qc.bed`). We want to run a linear association test adjusting for 10 PCs and age/sex, reporting 95% confidence intervals for beta coefficients, but we want to omit individual covariate result lines from the output text file to keep it compact. How should this command be constructed?
What was checked, without and with the skill
Checked
Without skill
With skill
Specifies `--linear hide-covar` for linear association analysis.
Passed
Passed
Requests 95% confidence interval estimation for beta coefficients.
Passed
Passed
Standardizes covariate variances across quantitative trait predictors.
Missed
Passed
case-13 · gemini-3.6-flash · 9/3/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md373 linessingle file
PLINK2 — GWAS and Population Genetics
Overview
PLINK2 is the high-performance successor to PLINK 1.9, designed for genome-wide association studies (GWAS) and population genetics analysis on large cohorts. It processes genotype data in PLINK binary format (.bed/.bim/.fam), VCF, and BGEN formats — performing sample and variant quality control (QC), kinship estimation, principal component analysis (PCA), and linear/logistic regression association testing. PLINK2 is 10–100× faster than PLINK 1.9 on most tasks due to multithreading and optimized I/O. Output files are compatible with downstream visualization (Manhattan/QQ plots) and meta-analysis tools.
When to Use
Running GWAS on a case-control or quantitative trait cohort after genotyping array QC
Performing sample QC: missingness, heterozygosity outliers, sex check, cryptic relatedness
Computing genome-wide LD pruning for PCA or relatedness estimation
Running PCA on genotype data to identify population stratification
Converting between PLINK binary, VCF, and BGEN formats
Filtering variants by MAF, HWE, missingness, or INFO score in VCF/imputed data
Use omics-plotting SKILL to render Manhattan and QQ plots from the association results
Use regenie or SAIGE instead for biobank-scale GWAS (>100k samples) requiring mixed model association to control for population structure
Use VCFtools as an alternative for VCF-specific population genetics statistics
Prerequisites
Software: PLINK2 (pre-compiled binary; no pip/conda package)
Input: PLINK binary files (.bed/.bim/.fam) or VCF/BGEN from array genotyping or imputation
Check before installing: The tool may already be available (e.g., inside a pixi / conda env). Always run command -v plink2 first and skip the install block if it returns a path. When executing tools inside a pixi project, prefer pixi run <tool> over plain <tool>.