Skip to content
TrustedSkillsbeta
Back to all skills

Requesting Code Review

Checked
lgbarn·vlatest·backend·MIT

Automates LG Barn code review requests via API, ensuring thorough testing and adherence to coding 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 lgbarn/devops-skills --skill requesting-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

lgbarn

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 lgbarn/devops-skills at 8a37cc3fe5.

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

Commit
8a37cc3fe5
Manifest
skills/requesting-code-review/SKILL.md
Scanned
2 files, 245 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 allows AI agents to request code reviews from designated individuals or teams. It facilitates a structured process for obtaining feedback on code changes, ensuring quality and adherence to coding standards. The agent can specify reviewers, provide context about the changes, and track review status.

When to use it

  • After completing a significant feature development task.
  • When refactoring existing codebase sections.
  • Before merging pull requests into main branches.
  • To proactively solicit feedback on complex or critical code implementations.

Key capabilities

  • Specifying reviewers (individuals or teams)
  • Providing context for the code changes
  • Tracking review status

Example prompts

  • "Request a code review from Alice and Bob for the 'user authentication' feature."
  • "Can you initiate a code review for my recent changes to the database connection module?"
  • "Please request a review of this pull request, highlighting the performance optimizations I made."

Tips & gotchas

This skill assumes access to a version control system (e.g., Git) and a code review platform integrated with that system. The agent's effectiveness depends on accurate reviewer identification and clear context provided in the request.