Zarr Python

What it does

When your agent uses it

• Format migration
• Scientific metadata preservation

Example prompts

• “Use the zarr-python skill to store and queries chunked N-D scientific arrays with Zarr-Python 3, including codecs, sharding, S3/GCS storage, and…”
• “/zarr-python”

Requirements

• Python 3
• Compatibility (from SKILL.md): Requires Python 3.12+ and zarr 3.4.0 with NumPy 2+. Remote I/O needs network access, zarr[remote] and the protocol backend; private stores need provider credentials. CLI migration needs zarr[cli].
• Pre-approved tools (allowed-tools): Read, Write, Edit, Bash

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

1. 1Inspect shape, dtype, axis names, coordinates, units, missing-value convention, format,
2. 2Choose chunks for actual selections and a memory budget. For sharding, choose shard
3. 3Create a new destination (overwrite=False or mode="w-"). Use mode="r" for
4. 4Write bounded blocks. Assign one writer per stored chunk, or per shard when
5. 5Reopen read-only and compare values, dtype, shape, coordinates, units, masks and
6. 6For a completed group hierarchy, optionally consolidate metadata. Format-3

What it can do on your machine

Read from SKILL.md and the folder at commit 92ace75. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves these tools, so the agent can use them without asking each time:

  • Read
  • Write
  • Edit
  • Bash

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Shell commands in SKILL.md call:

  • uv

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  Links to these hosts (documentation or services it may open):

  • zarr.readthedocs.io
  • arxiv.org
  • zarr-specs.readthedocs.io
  • github.com
  • doi.org
  • export.arxiv.org

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

• Compatibility

  Requires Python 3.12+ and zarr 3.4.0 with NumPy 2+. Remote I/O needs network access, zarr[remote] and the protocol backend; private stores need provider credentials. CLI migration needs zarr[cli].

  From compatibility in the SKILL.md frontmatter.

Context cost

Zarr Python loads about 2.2k tokens when it runs, and up to ~11k if it reads all its reference files. Until then it costs about 62 tokens; SKILL.md has 643 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~62

When it runs · the whole SKILL.md, loaded when a task matches

~2.2k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~11k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

• NotePre-approves every shell command (allowed-tools: Bash)SKILL.md

  allowed-tools: Read, Write, Edit, Bash

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from K-Dense-AI/scientific-agent-skills at commit 92ace75, republished under its MIT licence (© K-Dense-AI). 643 words, ~2,239 tokens.

name

zarr-python

description

Stores and queries chunked N-D scientific arrays with Zarr-Python 3, including codecs, sharding, S3/GCS storage, and NumPy/Dask/Xarray integration. Use for array layout, bounded I/O, format migration, or scientific metadata preservation.

allowed-tools

Read, Write, Edit, Bash

compatibility

Requires Python 3.12+ and zarr 3.4.0 with NumPy 2+. Remote I/O needs network access, zarr[remote] and the protocol backend; private stores need provider credentials. CLI migration needs zarr[cli].

license

MIT license

metadata.version

1.5

metadata.skill-author

K-Dense Inc.

metadata.last-reviewed

2026-10-01

metadata.upstream-version

3.4.0

uv pip install "zarr==3.4.0" "numpy==2.5.3"
# Optional remote backends and migration CLI:
uv pip install "zarr[remote,cli]==3.4.0" "fsspec==2026.9.0" "s3fs==2026.9.0" "gcsfs==2026.8.1"

import numpy as np
import zarr
from zarr.codecs import BloscCodec

expected = np.arange(96, dtype="float32").reshape(12, 8)
z = zarr.create_array(
    "array.zarr", shape=expected.shape, dtype=expected.dtype,
    chunks=(4, 4), zarr_format=3,
    compressors=BloscCodec(cname="zstd", clevel=5, shuffle="bitshuffle"),
    dimension_names=("sample", "feature"),
    attributes={"units": "arbitrary", "source": "synthetic example"},
)
for start in range(0, z.shape[0], 4):
    z[start:start + 4] = expected[start:start + 4]

reopened = zarr.open_array("array.zarr", mode="r")
np.testing.assert_array_equal(reopened[:], expected)
assert reopened.dtype == expected.dtype
assert reopened.metadata.dimension_names == ("sample", "feature")
assert reopened.attrs["units"] == "arbitrary"
subset = reopened[2:6, 1:4]  # Only this selection is materialized.

import numpy as np
import zarr

z = zarr.create_array(None, data=np.arange(80).reshape(10, 8), chunks=(2, 4))
zeros = zarr.zeros((10, 8), chunks=(2, 4), dtype="f4")
ones = zarr.ones((10, 8), chunks=(2, 4), dtype="f4")
filled = zarr.full((10, 8), fill_value=42, chunks=(2, 4), dtype="i4")
like = zarr.zeros_like(z)
np.testing.assert_array_equal(filled[:], np.full((10, 8), 42))

# Coordinate indexing pairs corresponding coordinates; orthogonal indexing is a product.
np.testing.assert_array_equal(z.vindex[[0, 5], [2, 7]], [2, 47])
np.testing.assert_array_equal(z.get_coordinate_selection(([0, 5], [2, 7])), [2, 47])
assert z.oindex[[0, 5], [2, 7]].shape == (2, 2)
assert z.blocks[0, 0].shape == (2, 4)
z[0, :] = np.arange(8)

import numpy as np
import zarr

series = zarr.create_array(None, shape=(0, 8), chunks=(2, 8), dtype="f4")
series.append(np.ones((2, 8), dtype="f4"), axis=0)
series.resize((4, 8))  # A tuple; append must match all non-appended dimensions.
assert series.shape == (4, 8)
np.testing.assert_array_equal(series[2:], np.zeros((2, 8)))

import numpy as np
import zarr

root = zarr.open_group("hierarchy.zarr", mode="w-", zarr_format=3)
temperature = root.create_group("temperature")
temp = temperature.create_array(
    "t2m", data=np.full((3, 4, 6), 280, dtype="f4"), chunks=(1, 4, 6),
    dimension_names=("time", "lat", "lon"), attributes={"units": "K"},
)
root.require_group("quality")
root.require_array("count", shape=(3,), chunks=(3,), dtype="i4")
root.attrs.update({"project": "synthetic climate example", "processing_version": "1.0"})
loaded = zarr.open_group("hierarchy.zarr", mode="r")
assert loaded["temperature/t2m"].attrs["units"] == "K"
assert loaded.attrs["processing_version"] == "1.0"
print(loaded.tree())  # Logical group/array tree, not physical metadata files.

© K-Dense-AI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 7 other files (references) in skills/zarr-python of K-Dense-AI/scientific-agent-skills.

• SKILL.md
• references/api_reference.md
• references/chunking_and_compression.md
• references/integration.md
• references/performance_and_patterns.md
• references/review.md
• references/storage_backends.md
• references/v3_migration.md

Open the folder on GitHubat commit 92ace75

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in K-Dense-AI/scientific-agent-skills, which our catalogue first saw on October 7, 2026.

• lingxling/awesome-skills-cnclaude-scientific-skills/skills/zarr-python