Visualization

Creates algorithmic art through p5.js generative sketches. Users describe a creative vision, and the skill generates an 'algorithmic philosophy' manifesto followed by interactive HTML/JS code that produces unique visual art using flow fields, particle systems, and mathematical patterns.

This skill enables Claude to create algorithmic and generative art using p5.js. It follows a two-step process: first creating an 'algorithmic philosophy' describing the aesthetic movement, then implementing it as interactive p5.js sketches with seeded randomness, flow fields, particle systems, and parametric controls.

Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.

This skill generates publication-quality scientific diagrams (flowcharts, neural network architectures, biological pathways, circuit diagrams) from natural language descriptions using AI. It features iterative quality refinement with different thresholds for journals, presentations, and posters.

A comprehensive reference skill for Vaex, a high-performance Python library for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Covers DataFrame operations, data loading, filtering, aggregations, machine learning pipelines, visualization, and performance optimization strategies.

This skill provides comprehensive documentation and guidance for using UMAP (Uniform Manifold Approximation and Projection), a fast dimensionality reduction technique for visualization and machine learning. It covers installation, parameter tuning, supervised/unsupervised learning, clustering preprocessing with HDBSCAN, and advanced features like Parametric UMAP and inverse transforms.

This skill provides a comprehensive toolkit for analyzing single-cell RNA-seq data using the scanpy library. It enables quality control, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, marker gene identification, cell type annotation, and publication-quality visualizations.

Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model.

A comprehensive health trend analysis skill that tracks weight/BMI, symptoms, medications, lab results, and mood/sleep patterns over time. It reads locally stored health data files and generates interactive HTML reports with ECharts visualizations, correlation analysis, and personalized health recommendations.

Analyzes CSV files, generates summary stats, and plots quick visualizations using Python and pandas.