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Code Review

Checked
eyadsibai·vlatest·backend

Automates Python/Node.js backend code reviews, identifying potential bugs, style issues, and security vulnerabilities.

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 eyadsibai/ltk --skill 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

eyadsibai

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 eyadsibai/ltk at f8e85697b9.

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

Commit
f8e85697b9
Manifest
plugins/ltk-engineering/skills/code-review/SKILL.md
Scanned
1 file, 163 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 provides automated code review capabilities. It analyzes submitted code, identifies potential bugs and style issues, and suggests improvements based on established coding standards. The tool aims to improve code quality and reduce the time spent in manual reviews.

When to use it

  • Before submitting a pull request to ensure adherence to project coding guidelines.
  • During pair programming sessions for real-time feedback and suggestions.
  • To quickly identify potential issues in newly written or modified code.
  • As part of a continuous integration/continuous delivery (CI/CD) pipeline for automated quality checks.

Key capabilities

  • Automated bug detection
  • Style guide enforcement
  • Code improvement suggestions
  • Adherence to coding standards

Example prompts

  • "Review this Python code snippet and suggest improvements."
  • "Analyze this JavaScript file for potential security vulnerabilities."
  • "Check this Go code against the project's style guidelines."

Tips & gotchas

The quality of the review depends on the clarity and completeness of the coding standards provided. Ensure that these standards are well-defined for optimal results.