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Pytest Code Review

Checked
existential-birds·vlatest·testing·Apache-2.0

Automates pytest test suite review, identifying missing tests, flaky assertions, and code style violations in Python backends.

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 existential-birds/beagle --skill pytest-code-review -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

existential-birds

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 existential-birds/beagle at d1a74899fb.

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

Commit
d1a74899fb
Manifest
plugins/beagle-python/skills/pytest-code-review/SKILL.md
Scanned
5 files, 1,160 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 pytest-code-review skill enables AI agents to automatically analyze Python test files for quality, coverage, and adherence to best practices using the Beagle framework. It provides immediate feedback on test structure, naming conventions, and assertion logic to ensure robust backend testing suites.

When to use it

  • Before merging pull requests that introduce new unit or integration tests.
  • When refactoring existing test code to improve readability and maintainability.
  • To validate that new features have corresponding test coverage before deployment.
  • During code reviews to catch flaky tests or missing edge cases early in the development cycle.

Key capabilities

  • Automated analysis of Python pytest files for structural integrity.
  • Detection of common anti-patterns and naming convention violations.
  • Evaluation of assertion logic quality within test functions.
  • Integration with the Beagle AI agent ecosystem for backend workflows.

Example prompts

  • "Run a code review on my new test_user_service.py file to check for best practices."
  • "Analyze these failing tests and suggest improvements to the assertions."
  • "Review the test coverage of my latest feature implementation using pytest standards."

Tips & gotchas

Ensure your project has pytest installed and configured correctly before invoking this skill. The agent focuses on code quality rather than execution results, so it may not flag runtime errors unless they are part of the static analysis context.