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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.
Passed 22 of 23 test cases with the skill, and 15 of 23 without it.
and 8 more in the full report
7 fixed15 passed either way1 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 7 test cases the skill fixed. Same model, same prompt, with and without the skill.
Provide the Python code to create a 2D Zarr array configured with Blosc compression using the 'zstd' compressor algorithm at level 5 with bitshuffle enabled, using the Zarr 3 codec API.
| Checked | Without skill | With skill |
|---|---|---|
| Configures compression using BloscCodec imported from zarr.codecs. | Passed | Passed |
| Passes the codec instance to the compressors parameter of the array creation function. | Missed | Passed |
The instructions your model reads when the skill loads.
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 zarr_format=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.
uv pip install "zarr==3.2.1"
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:
uv pip install "zarr[remote]==3.2.1" "s3fs==2026.4.0" "gcsfs==2026.5.0"
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.
import zarr
import numpy as np
# Create a 2D array with chunking and compression
z = zarr.create_array(
store="data/my_array.zarr",
shape=(10000, 10000),
chunks=(1000, 1000),
dtype="f4"
)
# Write data using NumPy-style indexing
z[:, :] = np.random.random((10000, 10000))
test cases passed, vs 15 / 23 without
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