Agent skill · software engineering · k-dense-ai
zarr-python
Chunked N-D arrays for cloud storage (Zarr-Python 3). Compressed arrays, parallel I/O, S3/GCS via fsspec, NumPy/Dask/Xarray compatible, for large-scale scientific computing pipelines.
Why this skill is useful
Provides specific commands and patterns for using Zarr with cloud storage that the AI wouldn't reliably generate on its own.
What it needs
Requires gcsfs, python, s3fs, zarr installed locally. About 4k tokens when loaded. Last updated 2026-08-03. 32,849 stars on the source repository.
What this skill does
Zarr Python Overview Zarr is a Python library for storing large N-dimensional arrays with chunking and compression. Apply this skill for efficient parallel I/O, cloud-native workflows, and seamless integration with NumPy, Dask, and Xarray. Current upstream: zarr 3.2.1 (released 2026-05-05). Docs: zarr.readthedocs.io. New arrays default to Zarr format 3; set zarrformat=2 for legacy interop. Zarr 3.2 adds rectilinear chunks and continues to refine the v3 codec pipeline. This skill is a community guide maintained by K-Dense Inc., not an official zarr-developers package. Quick Start Installation Requires Python 3.12+ and NumPy 2.0+ for current stable Zarr-Python. For remote stores (S3, GCS, HTTP), pin the optional extras/backends in your project lockfile: Use a version range such as zarr>=3,<4 only when your project has a committed lockfile and compatibility tests. For Zarr-Python 2 / Python 3.10–3.11 workflows, choose an exact zarr==2.x.y patch version from the support-v2 release notes and commit the resulting lockfile. Basic Array Creation Core Operations Creating Arrays Zarr provides multiple convenience functions for array creation: Opening Existing Arrays Reading and Writing Data Zarr arrays support NumPy-like indexing: Resizing and Appending Groups and Hierarchies Groups organize multiple arrays hierarchically, similar to directories or HDF5 groups. Creating and Using Groups Group API (v3) Use createarray / requirearray (h5py-style createdataset / requiredataset were removed in v3): Attributes and Metadata Attach custom metadata to arrays and groups using attributes: Important: Attributes must be JSON-serializable (strings, numbers, lists, dicts, booleans, null). Chunking, Compression, Storage, and Performance references/chunkingandcompression.md: sizing chunks to the access pattern (aim for ~1 MB, 5-100 MB on cloud), sharding, and codec choice. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills k-dense-ai/zarr-python