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Fastapi Code Review

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

Automates FastAPI code reviews, identifying potential errors, style issues, and security vulnerabilities quickly.

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 fastapi-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 failed 1 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • No undeclared network calls: failed

    Contacts 1 host that is neither well-known infrastructure nor declared in the skill's frontmatter: api.weather.com.

    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.

    Hosts contacted

    api.weather.comundeclared

    What it found

    • plugins/beagle-python/skills/fastapi-code-review/references/async.md:228contacts api.weather.com
      response = requests.get(f"https://api.weather.com/{city}")  # BLOCKS!

  • 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 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/fastapi-code-review/SKILL.md
Scanned
5 files, 929 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 fastapi-code-review skill enables AI agents to automatically analyze FastAPI application code for security vulnerabilities, performance bottlenecks, and adherence to best practices. It provides detailed feedback on dependency injection patterns, route definitions, and data validation logic within Python-based APIs.

When to use it

  • Before deploying a new API endpoint to production to catch critical errors early.
  • During the development phase to ensure consistent coding standards across a team.
  • When refactoring legacy FastAPI codebases to modernize architecture and improve maintainability.
  • To validate that input data validation schemas are correctly implemented for security.

Key capabilities

  • Automated static analysis of FastAPI source files.
  • Identification of common security flaws such as injection vulnerabilities.
  • Detection of performance issues in route handlers and dependencies.
  • Verification of proper use of Pydantic models for data validation.
  • Suggestions for improving error handling and response structures.

Example prompts

  • "Review this FastAPI router file for potential security risks and suggest fixes."
  • "Analyze the following code snippet for performance bottlenecks in database queries."
  • "Check if my dependency injection setup follows FastAPI best practices."

Tips & gotchas

Ensure your codebase is fully written in Python before running the analysis, as the tool relies on parsing Python syntax. While effective at catching common issues, complex architectural decisions may still require human judgment to interpret context-specific trade-offs.