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Python Testing

Checked
autumnsgrove·vlatest·backend

Helps with Python, testing as part of developing backend services and APIs workflows.

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 autumnsgrove/lattice --skill python-testing -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

autumnsgrove

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 autumnsgrove/groveengine at edebdc71a7.

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

Commit
edebdc71a7
Manifest
.claude/skills/python-testing/SKILL.md
Scanned
1 file, 155 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 python-testing skill enables AI agents to execute, debug, and validate Python code directly within their environment. It supports running scripts, analyzing syntax errors, and verifying logic against specific inputs without requiring manual setup.

When to use it

  • Debugging complex Python scripts where standard linters fail to catch runtime logic errors.
  • Validating data processing pipelines before deploying them to production environments.
  • Running unit tests or integration checks on new code snippets generated by the agent.
  • Verifying that specific functions return expected outputs given particular input parameters.

Key capabilities

  • Direct execution of Python scripts and modules in a sandboxed environment.
  • Real-time syntax checking and error reporting with detailed stack traces.
  • Input validation to ensure code behaves correctly under varied conditions.
  • Support for standard library imports and common third-party packages.

Example prompts

  • "Run this Python script that calculates Fibonacci numbers and show me the output for the first 10 terms."
  • "Debug this function that crashes when given an empty list as input."
  • "Test this data cleaning pipeline with a sample CSV containing missing values and report any errors."

Tips & gotchas

Ensure your Python environment has necessary dependencies installed before requesting complex library operations. While the skill handles standard logic well, very large scripts may hit execution time limits or memory constraints during testing.