Agent skill · data analytics · aiskillstore
anndata
Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
Why this skill is useful
Provides specific commands and patterns for handling .h5ad files and integrating with the scverse ecosystem that the AI wouldn't reliably generate on its own.
What it needs
Requires anndata installed locally. About 5k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
AnnData Overview AnnData is a Python package for handling annotated data matrices, storing experimental measurements (X) alongside observation metadata (obs), variable metadata (var), and multi-dimensional annotations (obsm, varm, obsp, varp, uns). Originally designed for single-cell genomics through Scanpy, it now serves as a general-purpose framework for any annotated data requiring efficient storage, manipulation, and analysis. When to Use This Skill Use this skill when: Creating, reading, or writing AnnData objects Working with h5ad, zarr, or other genomics data formats Performing single-cell RNA-seq analysis Managing large datasets with sparse matrices or backed mode Concatenating multiple datasets or experimental batches Subsetting, filtering, or transforming annotated data Integrating with scanpy, scvi-tools, or other scverse ecosystem tools Installation Quick Start Creating an AnnData object Reading data Writing data Basic operations Core Capabilities 1. Data Structure Understand the AnnData object structure including X, obs, var, layers, obsm, varm, obsp, varp, uns, and raw components. See: references/datastructure.md for comprehensive information on: Core components (X, obs, var, layers, obsm, varm, obsp, varp, uns, raw) Creating AnnData objects from various sources Accessing and manipulating data components Memory-efficient practices 2. Input/Output Operations Read and write data in various formats with support for compression, backed mode, and cloud storage. See: references/iooperations.md for details on: Native formats (h5ad, zarr) Alternative formats (CSV, MTX, Loom, 10X, Excel) Backed mode for large datasets Remote data access Format conversion Performance optimization Common commands: 3. Concatenation Combine multiple AnnData objects along observations or variables with flexible join strategies. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/anndata--dfca7d