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Update SKILL.md files to add double quotation marks for all skills, ensuring clarity and consistency across all entries.

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Haoxuan "Orion" Li
2025-10-20 20:51:50 -07:00
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scientific-packages/umap-learn/SKILL.md
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---
name: umap-learn
description: Comprehensive guide for UMAP (Uniform Manifold Approximation and Projection) - a fast, scalable dimensionality reduction technique for visualization, clustering, and machine learning. Use this skill for: dimensionality reduction of high-dimensional datasets (genes, proteins, images, text embeddings, sensor data), creating 2D/3D visualizations of complex data, preprocessing data for clustering algorithms (especially HDBSCAN), supervised and semi-supervised dimensionality reduction with labels, transforming new data using trained UMAP models, parametric UMAP with neural networks, feature engineering for downstream ML models, manifold learning and non-linear dimensionality reduction, comparing UMAP to t-SNE/PCA/other methods, inverse transforms and data reconstruction, aligned UMAP for temporal/batch data analysis. Triggers include: "dimensionality reduction", "UMAP", "manifold learning", "data visualization", "clustering preprocessing", "high-dimensional data", "embedding", "reduce dimensions", "2D visualization", "3D visualization", "supervised dimensionality reduction", "parametric UMAP", "transform new data", "feature engineering", "HDBSCAN clustering", "t-SNE alternative", "non-linear dimensionality reduction", "inverse transform", "data reconstruction", "aligned embeddings", "batch effect correction", "temporal data analysis".
description: "Comprehensive guide for UMAP (Uniform Manifold Approximation and Projection) - a fast, scalable dimensionality reduction technique for visualization, clustering, and machine learning. Use this skill for: dimensionality reduction of high-dimensional datasets (genes, proteins, images, text embeddings, sensor data), creating 2D/3D visualizations of complex data, preprocessing data for clustering algorithms (especially HDBSCAN), supervised and semi-supervised dimensionality reduction with labels, transforming new data using trained UMAP models, parametric UMAP with neural networks, feature engineering for downstream ML models, manifold learning and non-linear dimensionality reduction, comparing UMAP to t-SNE/PCA/other methods, inverse transforms and data reconstruction, aligned UMAP for temporal/batch data analysis. Triggers include: \"dimensionality reduction\", \"UMAP\", \"manifold learning\", \"data visualization\", \"clustering preprocessing\", \"high-dimensional data\", \"embedding\", \"reduce dimensions\", \"2D visualization\", \"3D visualization\", \"supervised dimensionality reduction\", \"parametric UMAP\", \"transform new data\", \"feature engineering\", \"HDBSCAN clustering\", \"t-SNE alternative\", \"non-linear dimensionality reduction\", \"inverse transform\", \"data reconstruction\", \"aligned embeddings\", \"batch effect correction\", \"temporal data analysis\"."
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# UMAP-Learn