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Python Code Review

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

Helps with Python, code review 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 existential-birds/beagle --skill python-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/python-code-review/SKILL.md
Scanned
6 files, 747 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

python-code-review

What it does

This skill enables AI agents to analyze Python code for quality, security, and best practices. It provides automated feedback on syntax errors, logical flaws, and adherence to coding standards within backend development workflows.

When to use it

  • Before deploying production scripts to catch critical bugs early in the lifecycle.
  • During team collaboration to enforce consistent style guides across multiple developers.
  • When auditing third-party libraries or open-source dependencies for potential vulnerabilities.
  • To generate actionable refactoring suggestions that improve code readability and maintainability.

Key capabilities

  • Identifies syntax errors and logical inconsistencies in Python scripts.
  • Enforces PEP 8 compliance and other community coding standards.
  • Detects common security vulnerabilities such as injection flaws or unsafe deserialization.
  • Suggests performance optimizations and cleaner architectural patterns.
  • Provides detailed comments explaining the reasoning behind each suggestion.

Example prompts

  • "Review this Flask application for potential SQL injection risks and suggest fixes."
  • "Analyze the following Python module for PEP 8 violations and propose a refactored version."
  • "Check this data processing script for memory leaks and inefficient loops."

Tips & gotchas

Ensure your AI agent has access to the full context of the codebase, as isolated snippets may miss broader architectural issues. While the skill catches many common errors, it should complement—not replace—human review for complex business logic or novel algorithms.