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.
npx agentmods add skills/nevergoodstudy-hub/wechat-article-summarizer/anndatanpx skills add nevergoodstudy-hub/wechat-article-summarizer --skill anndatagit clone --depth 1 https://github.com/nevergoodstudy-hub/wechat-article-summarizerWhat 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.
| Model | Per session | Once 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 |
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.
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.
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/')
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.
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.
- yesterday First seen · 399 lines · 63 tokens per session scan A c30b964e4ac4
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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