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Numpy Best Practices

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mindrally·vlatest·data·Apache-2.0

Optimize NumPy code for performance and readability using mindrally's best practices guidance.

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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 mindrally/skills --skill numpy-best-practices -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

mindrally

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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

    No encoded or obfuscated payloads.

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  • No credential access: passed

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  • Pinned to a commit: passed

    Scanned mindrally/skills at 7682ca7771.

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Commit
7682ca7771
Manifest
numpy-best-practices/SKILL.md
Scanned
1 file, 98 lines
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About this skill

The numpy-best-practices skill equips AI agents with optimized strategies for performing high-performance numerical computations using the NumPy library. It ensures code efficiency by enforcing vectorization, proper memory management, and best coding standards specific to scientific Python workflows.

When to use it

  • Optimize computational bottlenecks: When an agent needs to process large datasets faster than standard Python loops allow.
  • Ensure numerical stability: During complex mathematical simulations where precision and overflow protection are critical.
  • Standardize scientific codebases: To maintain consistency and readability across different parts of a data science project.
  • Reduce memory footprint: When working with limited resources or handling massive arrays that require efficient storage strategies.

Key capabilities

  • Enforces vectorized operations to eliminate slow Python loops.
  • Implements efficient memory allocation for large multidimensional arrays.
  • Applies broadcasting rules correctly to simplify complex mathematical expressions.
  • Utilizes advanced indexing and slicing techniques for data manipulation.
  • Integrates with standard scientific computing workflows (e.g., SciPy, Pandas).

Example prompts

  • "Refactor this Python loop into a vectorized NumPy operation to improve execution speed."
  • "Generate a memory-efficient NumPy array structure for storing time-series sensor data."
  • "Apply NumPy broadcasting rules to calculate element-wise differences between two matrices of different shapes."

Tips & gotchas

Ensure the target environment has NumPy installed before deploying agents relying on this skill, as it is not included in base Python distributions. While vectorization offers speed gains, be cautious with extremely large arrays that may exceed available RAM; consider using numpy.memmap for out-of-core processing if memory limits are tight.