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Code Reviewer

Checked
u9401066·vlatest·backend·Apache-2.0

This AI analyzes your backend code for potential bugs and style issues, boosting code quality and reducing errors.

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 u9401066/med-paper-assistant --skill code-reviewer -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

u9401066

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 u9401066/med-paper-assistant at 7e1eab0b05.

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

Commit
7e1eab0b05
Manifest
.claude/skills/code-reviewer/SKILL.md
Scanned
1 file, 39 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

The code-reviewer skill analyzes provided code snippets and identifies potential issues. It can find bugs, suggest improvements to code style, and offer recommendations for increased efficiency. The skill aims to improve code quality through automated feedback, helping developers catch errors early in the development process.

When to use it

  • Before committing code: Run the reviewer on your changes before pushing them to a repository.
  • During code reviews: Use it to quickly identify common issues and focus discussions on more complex problems.
  • Learning new languages/frameworks: Get feedback on your code as you learn, ensuring best practices are followed.
  • Refactoring existing code: Identify areas for improvement in legacy codebases.

Key capabilities

  • Bug detection
  • Code style suggestions
  • Efficiency recommendations

Example prompts

  • "Review this Python function and suggest improvements: [code snippet]"
  • "Can you find any potential bugs in this JavaScript code? [code snippet]"
  • "Analyze this C++ code for efficiency and readability. [code snippet]"

Tips & gotchas

The skill's effectiveness depends on the quality of the provided code; poorly formatted or incomplete snippets may yield less accurate results. It is best used as a supplementary tool alongside manual review, not a replacement.