anndata

A Python data structure for annotated matrices: tables of measurements with labels and related metadata. It is commonly used for single-cell genomics data and stored in formats such as .h5ad.

In plain words
What is it for?
It helps create, read, write, filter, combine, and transform annotated datasets, including data used with Scanpy and other scverse tools.
Why use it?
It keeps measurements, cell or sample information, feature information, and related analysis results together in one object.

Skill for Claude CodeCodex

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.

agentmods
npx agentmods add skills/nevergoodstudy-hub/wechat-article-summarizer/anndata
Any agent
npx skills add nevergoodstudy-hub/wechat-article-summarizer --skill anndata
Clone the repo
git clone --depth 1 https://github.com/nevergoodstudy-hub/wechat-article-summarizer

Made for: Claude Code, Codex.

Per session 63 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,760 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 89% 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 $0.00063 $0.02760
Opus 5 $0.00032 $0.01380
Sonnet 5 $0.00013 $0.00552
Haiku 4.5 $0.00006 $0.00276

Measured yesterday against content hash c30b964e4ac4, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, 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 yesterday.

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

89% identical to anndata — 71 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.

.warp/skills/anndata/SKILL.md · 399 lines

How it starts

The opening of the file, as written. The whole thing — 399 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 · 399 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. yesterday First seen · 399 lines · 63 tokens per session scan A c30b964e4ac4

Subscribe to this mod's changes

anndata is a skill published in the GitHub repository nevergoodstudy-hub/wechat-article-summarizer (5 stars, last pushed 2mo ago), licensed MIT. It adds 63 tokens to every session and 2,760 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to anndata, differing in 71 lines, and is treated as a copy.

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