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Code Review Checklist

Checked
liuchiawei·vlatest·backend

Automates backend code reviews using a comprehensive checklist tailored to identify common errors and ensure quality standards.

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 liuchiawei/agent-skills --skill code-review-checklist -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

liuchiawei

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 liuchiawei/agent-skills at f5a7853d0d.

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

Commit
f5a7853d0d
Manifest
skills/review/code-review-checklist/SKILL.md
Scanned
1 file, 433 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 a structured checklist to guide AI agents through code review processes. It helps ensure thoroughness and consistency in evaluating code changes, covering aspects like functionality, readability, and potential security vulnerabilities. The agent can use this checklist to systematically assess pull requests or code submissions.

When to use it

  • Reviewing pull requests for backend services before merging into the main branch.
  • Evaluating contributions from external developers on a project.
  • Performing automated checks during continuous integration pipelines.
  • Ensuring adherence to coding standards and best practices within a team.

Key capabilities

  • Provides a detailed code review checklist.
  • Supports consistent evaluation of code changes.
  • Helps identify potential bugs, security vulnerabilities, and readability issues.

Example prompts

  • "Review this pull request: [link to pull request]"
  • "Generate a code review checklist for this file: [file content]"
  • "Perform a code review focusing on security best practices."

Tips & gotchas

The skill's effectiveness relies on providing the AI agent with sufficient context, such as the relevant code or a link to a pull request. Ensure the agent has access to project-specific coding standards for optimal results.