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Code Review Assistant

Checked
fusionet24·vlatest·backend·MIT

Automates backend code reviews, identifying potential bugs, security vulnerabilities, and style inconsistencies for faster, higher quality deployments.

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 fusionet24/aiskills --skill code-review-assistant -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

fusionet24

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 fusionet24/aiskills at 65b3141088.

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

Scanned
1 file, 91 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

What it does

This skill acts as an automated code review assistant. It analyzes provided code snippets, identifies potential bugs and style issues, and suggests improvements based on common coding best practices. The tool aims to accelerate the development process by providing immediate feedback and reducing the burden of manual code reviews.

When to use it

  • Early-stage bug detection: Before submitting a pull request, quickly check your code for common errors.
  • Code style enforcement: Ensure consistency in coding style across a project.
  • Learning new languages/frameworks: Get feedback on your code as you learn a new technology.
  • Refactoring assistance: Identify areas where existing code can be improved for readability and efficiency.

Key capabilities

  • Automated bug detection
  • Style issue identification
  • Suggestion of improvements
  • Code analysis based on best practices

Example prompts

  • "Review this Python function for potential errors: [code snippet]"
  • "Check the style of this JavaScript code and suggest improvements."
  • "Can you find any bugs or inefficiencies in this Java class?"

Tips & gotchas

The skill's effectiveness depends on the quality and clarity of the provided code. It is best suited for relatively small to medium-sized code snippets.