Skip to content
TrustedSkillsbeta
Back to all skills

Code Review

Checked
diskd-ai·vlatest·backend

Helps with AI, code review as part of developing backend services and APIs workflows.

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 diskd-ai/code-review --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

diskd-ai

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 diskd-ai/code-review at ec5e3bf805.

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

Commit
ec5e3bf805
Manifest
SKILL.md
Scanned
10 files, 1,021 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, identifying potential bugs and suggesting improvements. It analyzes code based on established best practices and common coding standards to ensure quality and maintainability. The tool aims to reduce the burden of manual code reviews for developers and improve overall code quality.

When to use it

  • Before submitting a pull request: Quickly identify issues before wider team review.
  • During onboarding new developers: Ensure adherence to project-specific coding standards.
  • For refactoring existing code: Evaluate changes against established best practices.
  • Automated CI/CD pipelines: Integrate into your workflow for continuous quality checks.

Key capabilities

  • Bug detection
  • Coding standard enforcement
  • Improvement suggestions
  • Code analysis based on best practices

Example prompts

  • "Review this Python code for potential errors and style issues."
  • "Analyze the following JavaScript function and suggest improvements to its efficiency."
  • "Check this Go file against our team's coding guidelines."

Tips & gotchas

The skill’s effectiveness depends on clear project-specific coding standards being provided or inferred. It is designed for backend code, so results may be less accurate when applied to frontend code.