anndata

anndata is a skill for Claude Code, Codex from CHENyiru3/AI-Skills-Collections. It costs 63 tokens per session (2,925 once invoked), scanned A, a copy of anndata, MIT.

A Python data structure for storing a matrix together with labels and related annotations. It is commonly used for single-cell genomics, where rows and columns represent measured biological data and its metadata.

In plain words
What is it for?
Use it to create, read, write, filter, combine, or transform annotated datasets in Python, especially in the scverse single-cell analysis ecosystem.
Why use it?
It keeps measurements and their sample, feature, and analysis metadata together, including data stored in formats such as h5ad and zarr.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to create, read, write, filter, combine, or transform annotated datasets in Python, especially in the scverse single-cell analysis ecosystem.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/chenyiru3/ai-skills-collections/anndata
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add CHENyiru3/AI-Skills-Collections --skill anndata
Clone the repo
git clone --depth 1 https://github.com/CHENyiru3/AI-Skills-Collections

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for anndata

README.md
[![agentmods](https://agentmods.dev/badge/skills/chenyiru3/ai-skills-collections/anndata/github.svg)](https://agentmods.dev/skills/chenyiru3/ai-skills-collections/anndata)
Your own site
<a href="https://agentmods.dev/skills/chenyiru3/ai-skills-collections/anndata"><img src="https://agentmods.dev/badge/skills/chenyiru3/ai-skills-collections/anndata/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for anndata

Your own site · 80×15
<a href="https://agentmods.dev/skills/chenyiru3/ai-skills-collections/anndata"><img src="https://agentmods.dev/badge/skills/chenyiru3/ai-skills-collections/anndata.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 63 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,925 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 84% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00063 $0.02925
Opus 5.5 $0.00025 $0.01170
Sonnet 5.5 $0.00013 $0.00585
Haiku 4.5 $0.00006 $0.00293

Measured 6d ago against content hash 5521453a8097, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

anndata scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

84% identical to anndata — 192 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

skills-market/compbio/single-cell/analysis/anndata/SKILL.md · 401 lines

How it starts

The opening of the file, as written. The whole thing — 401 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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

uv pip install anndata

# With optional dependencies
uv pip install anndata[dev,test,doc]

Quick Start

Creating an AnnData object

import anndata as ad
import numpy as np
import pandas as pd

# Minimal creation
X = np.random.rand(100, 2000)  # 100 cells × 2000 genes
adata = ad.AnnData(X)

# With metadata
obs = pd.DataFrame({
    'cell_type': ['T cell', 'B cell'] * 50,
    'sample': ['A', 'B'] * 50
}, index=[f'cell_{i}' for i in range(100)])

var = pd.DataFrame({
    'gene_name': [f'Gene_{i}' for i in range(2000)]
}, index=[f'ENSG{i:05d}' for i in range(2000)])

adata = ad.AnnData(X=X, obs=obs, var=var)

Reading data

# Read h5ad file
adata = ad.read_h5ad('data.h5ad')

# Read with backed mode (for large files)
adata = ad.read_h5ad('large_data.h5ad', backed='r')

# Read other formats
adata = ad.read_csv('data.csv')
adata = ad.read_loom('data.loom')
adata = ad.read_10x_h5('filtered_feature_bc_matrix.h5')

Writing data

# Write h5ad file
adata.write_h5ad('output.h5ad')

# Write with compression
adata.write_h5ad('output.h5ad', compression='gzip')

# Write other formats
adata.write_zarr('output.zarr')
adata.write_csvs('output_dir/')

Read the full file on GitHub · 401 lines

Files

What ships with it

5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 6d ago First seen · 401 lines · 63 tokens per session scan A 5521453a8097

Subscribe to this mod's changes

anndata is a skill published in the GitHub repository CHENyiru3/AI-Skills-Collections (1 stars, last pushed 7d ago), licensed MIT. It adds 63 tokens to every session and 2,925 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 84% identical to anndata, differing in 192 lines, and is treated as a copy.

Related

Other skills, from other repositories

jupyter-notebook

Iterative Python via live Jupyter kernel (hamelnb).

NousResearch/hermes-agent · 18 tokens

matlab

Builds, reviews, migrates, and plans MATLAB or GNU Octave numerical workflows. Use for arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.

K-Dense-AI/scientific-agent-skills · 44 tokens

pennylane

Builds and differentiates PennyLane quantum circuits, hybrid PyTorch or JAX models, molecular VQE and QAOA workflows. Use for variational quantum algorithms, quantum machine learning, simulator validation, and moving validated circuits to provider plugins. For hardware-specific compilation use qiskit or cirq; for…

K-Dense-AI/scientific-agent-skills · 74 tokens

cuopt-numerical-optimization-api

LP, MILP, and QP (beta) with cuOpt — Python, C, and CLI. Use when the user is solving LP, MILP, or QP with any cuOpt interface.

NVIDIA/skills · 51 tokens

rocm-kernels

Provides guidance for writing and benchmarking optimized Triton kernels for AMD GPUs (MI355X, R9700) on ROCm, targeting HuggingFace diffusers (LTX-Video, SD3, FLUX) and transformers. Core kernels: RMSNorm, RoPE 3D, GEGLU, AdaLN. Includes XCD swizzle, autotune, diffusers integration patterns, and LTX-Video pipeline…

huggingface/kernels · 93 tokens

pymatgen-materials

Materials science computation with pymatgen. Use when: (1) crystal structure creation and manipulation, (2) phase diagram construction, (3) electronic structure analysis, (4) symmetry and space group operations, (5) VASP input/output parsing. NOT for: molecular chemistry (use rdkit-chemistry), protein structure (use…

beita6969/ScienceClaw · 91 tokens