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Scipy Best Practices

Checked
mindrally·vlatest·devops·Apache-2.0

Ensures SciPy code adheres to best practices—optimization, testing, documentation—for robust and maintainable data science pipelines.

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 mindrally/skills --skill scipy-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

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

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.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned mindrally/skills at 7682ca7771.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
7682ca7771
Manifest
scipy-best-practices/SKILL.md
Scanned
1 file, 111 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

The scipy-best-practices skill equips AI agents with optimized workflows for scientific computing, ensuring efficient execution of complex mathematical operations and data analysis tasks. It standardizes interactions with SciPy libraries to maximize performance and code reliability in production environments.

When to use it

  • Automating the generation of high-performance numerical algorithms for engineering simulations.
  • Streamlining statistical modeling and optimization routines within DevOps pipelines.
  • Ensuring consistent, best-practice implementations when integrating scientific Python libraries into agent workflows.
  • Reducing computational overhead in data-heavy analytical processes by leveraging optimized SciPy configurations.

Key capabilities

  • Optimized numerical computation execution
  • Standardized SciPy library integration patterns
  • Enhanced performance for mathematical operations
  • Reliable statistical analysis workflows

Example prompts

  • "Configure the agent to solve differential equations using best-practice SciPy methods."
  • "Optimize this data processing pipeline by applying scipy-best-practices for numerical stability."
  • "Generate a robust optimization script that follows established SciPy conventions for production use."

Tips & gotchas

Ensure your environment has the latest SciPy version installed to fully leverage these optimized practices. This skill is specifically designed for DevOps contexts involving scientific computing, so it may not apply to general web development tasks.