UMAP agent skills for Claude Code, Codex and other agents.

Analyze single-cell data with core Scarf, the out-of-core Zarr DataStore library with immutable artifacts and pipeline runs.

NygenAnalytics/scarf

A skill your agent uses for GPU-accelerated machine learning on tabular data using NVIDIA cuML.

wahyudesu/Fastapi-AI-Production-Template

UMAP dimensionality reduction. An agent skill from davila7/claude-code-templates.

davila7/claude-code-templates

Performs Scanpy single-cell RNA-seq QC, normalization, HVG selection, PCA/UMAP/t-SNE, clustering, exploratory marker ranking, pseudobulk preparation, visualization, and Seurat or…

K-Dense-AI/scientific-agent-skills

BPX level command skill. An agent skill from wilddogjp/openbpx.

wilddogjp/openbpx

Applies UMAP-learn to nonlinear dimensionality reduction, 2D/3D embeddings, clustering preprocessing, supervised or semi-supervised UMAP, DensMAP, AlignedUMAP, and Parametric UMAP workflows.

K-Dense-AI/scientific-agent-skills

Produce and interpret PCA, t-SNE, UMAP, and PHATE plots for high-dimensional omics data with rigor about which method preserves what (variance, local structure, manifold, transitions)…

GPTomics/bioSkills

Stage 2 of the spatial transcriptomics workflow — normalize 10x Visium data and cluster spatial spots.

QING1105/ezST

A skill your agent uses when performing sample-level dimensionality reduction and visualization on abundance or OTU-style matrices with a companion group file, generating UMAP and/or t-SNE…

aipoch/medical-research-skills

Unsupervised clustering and cell-type identification for high-dimensional flow, spectral, and mass cytometry - FlowSOM, PhenoGraph, FlowSOM-via-CATALYST, with UMAP/tSNE for visualization.

GPTomics/bioSkills

Dimensionality reduction and graph-based clustering for single-cell RNA-seq with Scanpy (Python) and Seurat (R).

GPTomics/bioSkills

Plots spatial transcriptomics expression, clusters, and annotations on tissue using Squidpy and Scanpy.

GPTomics/bioSkills

Integrates single-cell paired TCR/BCR (10x VDJ, AIRR, dandelion, BD Rhapsody) with gene expression in an AnnData/MuData object using scirpy - chain-pairing QC, clonotype definition, clonal…

GPTomics/bioSkills

Dimensionality reduction and clustering for single-cell RNA-seq using Seurat (R) and Scanpy (Python).

majiayu000/claude-skill-registry

Standard single-cell RNA-seq analysis pipeline. An agent skill from aipoch/medical-research-skills.

aipoch/medical-research-skills

scRNA-seq with Scanpy: QC, normalization, HVG selection, PCA, neighborhood graph, UMAP/t-SNE, Leiden clustering, markers, cell annotation, trajectory inference.

jaechang-hits/SciAgent-Skills

Harmony batch correction for scRNA-seq and other omics. An agent skill from jaechang-hits/SciAgent-Skills.

jaechang-hits/SciAgent-Skills

Machine learning: clustering, PCA/t-SNE/UMAP, classification, prediction regression (Ridge/Lasso/ensemble), cross-validation, Pipelines.

brycewang-stanford/Auto-Empirical-Research-Skills

A skill your agent uses whenever the user wants reproducible CS/AI experiments, model evaluation, regression/classification/clustering analyses, bioinformatics workflows, QC, differential…

Citrus-bit/Anaxa

Data science methodology for Python research: EDA, validation, causal inference (IV, DiD, RD, synthetic control), clustering/PCA/UMAP, supervised ML, geospatial, visualization.

brycewang-stanford/Auto-Empirical-Research-Skills

Load when building the neighbour graph, embedding (UMAP/t-SNE/diffmap/PHATE), and clustering (Leiden/Louvain) on a normalised single-cell AnnData.

TianGzlab/OmicsClaw

Load when preprocessing a single-cell ATAC peak × cell AnnData via Signac-style TF-IDF + LSI + Leiden, producing a clustered UMAP-ready object.

TianGzlab/OmicsClaw

Load when inferring copy-number variation per spot on a preprocessed spatial AnnData with chromosome-annotated genes via infercnvpy (default — log-ratio sliding-window) or Numbat (R, allele-aware…

TianGzlab/OmicsClaw

Load when removing batch effects across multiple spatial samples on a multi-batch spatial AnnData via Harmony, BBKNN, or Scanorama before downstream analysis.

TianGzlab/OmicsClaw

Load when running the foundational spatial transcriptomics QC + filtering + normalisation + HVG + PCA + neighbour-graph + Leiden pipeline on a Visium / Xenium / generic spatial AnnData.

TianGzlab/OmicsClaw

Load when estimating RNA velocity on a spatial AnnData with layers["spliced"] + layers["unspliced"] via scVelo (stochastic / deterministic / dynamical) or veloVI (deep generative).

TianGzlab/OmicsClaw

End-to-end single-cell RNA-seq workflow from 10X Genomics data to annotated cell types.

majiayu000/claude-skill-registry

UMAP dimensionality reduction for visualization, clustering prep, and feature engineering.

jaechang-hits/SciAgent-Skills