skillboardUpdated every 6 hours
skillboardMachine learning: clustering, PCA/t-SNE/UMAP, classification, prediction regression (Ridge/Lasso/ensemble), cross-validation, Pipelines. For unsupervised analysis, classification, or prediction. For econometric regression use pyfixest/statsmodels.
scikit learn Skill General purpose machine learning with scikit learn. Covers unsupervised methods (clustering, GMM, PCA, t SNE, UMAP, manifold learning, evaluation metrics), supervised methods (classification, prediction focused regression via Ridge/Lasso/ensemble methods, model evaluation, cross validation), and shared infrastructure (preprocessing, Pipeline construction, feature selection). Use when performing cluster analysis, dimension reduction, classification, prediction focused…
Full body not shown for this license – view the source on GitHub →Copy a source-pinned command for your client. You run it yourself.
Destination: .claude/skills/scikit-learn · pinned to the source commit
# Run from your project root
git clone https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills.git .skillboard-tmp
git -C .skillboard-tmp checkout 692e9fa3fea40bbdf614584d461851f8fb968ac2
mkdir -p ".claude/skills"
cp -r ".skillboard-tmp/skills/17-DAAF-Contribution-Community-daaf/dot-claude/skills/scikit-learn" ".claude/skills/"
rm -rf .skillboard-tmpReview the source before running. This copies files into your project; it is not a one-click install and does not verify runtime safety.
sudo apt update && sudo apt install -y gitnpm install -g @anthropic-ai/claude-code# Run from your project root
git clone https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills.git .skillboard-tmp
git -C .skillboard-tmp checkout 692e9fa3fea40bbdf614584d461851f8fb968ac2
mkdir -p ".claude/skills"
cp -r ".skillboard-tmp/skills/17-DAAF-Contribution-Community-daaf/dot-claude/skills/scikit-learn" ".claude/skills/"
rm -rf .skillboard-tmpDestination: .claude/skills/scikit-learn
Scanner static-checks@0.1.0 · commit 692e9fa3fea4. Static checks cannot prove runtime safety – review the source and the exact diff before installing. How checks work.
No static rules matched. This is not a safety guarantee.