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Zarr Python

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jimmc414·vlatest·backend

Reads, writes, and manipulates Zarr data arrays in Python for efficient large dataset handling.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add jimmc414/kosmos --skill zarr-python -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

jimmc414

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Install target

Live repository

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No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

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  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

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  • Pinned to a commit: passed

    Scanned jimmc414/kosmos at 6cfe7f690d.

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Commit
6cfe7f690d
Manifest
kosmos-claude-scientific-skills/scientific-skills/zarr-python/SKILL.md
Scanned
2 files, 1,138 lines
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About this skill

What it does

This skill provides Python bindings to the Zarr library, enabling efficient storage and retrieval of large, multi-dimensional arrays. It allows for chunked data organization within cloud object stores like AWS S3 or Google Cloud Storage. The skill facilitates working with datasets too large to fit in memory by streaming data directly from these remote locations.

When to use it

  • Analyzing satellite imagery stored in Zarr format.
  • Processing scientific simulations that generate massive numerical datasets.
  • Building a data pipeline for serving large arrays to machine learning models.
  • Working with genomic data stored as multi-dimensional arrays in the cloud.

Key capabilities

  • Python bindings for Zarr library functions.
  • Support for chunked array storage.
  • Integration with cloud object stores (e.g., S3, GCS).
  • Streaming of large datasets from remote locations.

Example prompts

  • "Read the data from this Zarr dataset: s3://my-bucket/data.zarr into a NumPy array."
  • "Write this NumPy array to a new Zarr dataset at gs://my-bucket/output.zarr with chunk size 1024x1024."
  • “List all the datasets in this Zarr store: s3://my-bucket/store.”

Tips & gotchas

  • Requires Python and the Zarr library to be installed in the environment.
  • Ensure proper authentication credentials are configured for accessing cloud storage.