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Receiving Code Review

Checked
lgbarn·vlatest·backend·MIT

Automates initial code review checks using lgbarn's models, flagging potential issues for faster, more focused human feedback.

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 receiving-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/receiving-code-review/SKILL.md
Scanned
1 file, 190 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 an AI agent to process and understand code review feedback, identify areas for improvement based on reviewer comments, and suggest potential solutions. It can parse common code review platforms and extract actionable insights from suggestions regarding style, functionality, or security vulnerabilities. The agent can then summarize the key concerns raised by reviewers and propose modifications to address them.

When to use it

  • Automated Code Review Response: When a developer needs help responding to a large number of comments in a code review.
  • Learning Best Practices: To understand why certain coding styles or approaches are preferred based on reviewer feedback.
  • Identifying Potential Bugs/Vulnerabilities: When a code review highlights potential issues that the original author may have missed.
  • Improving Code Quality: To proactively identify and address areas for improvement in your codebase, even before formal reviews.

Key capabilities

  • Parses code review comments from various platforms (unspecified).
  • Identifies actionable items within reviewer feedback.
  • Summarizes key concerns raised by reviewers.
  • Suggests potential solutions or modifications to the code.

Example prompts

  • "Here's a link to my GitHub pull request. Can you summarize the main points of the review and suggest how I can address them?"
  • "I received this code review feedback: [paste review text]. What are the most important things I should focus on improving?"
  • "This reviewer mentioned a potential security vulnerability in my code. Explain what they mean and how to fix it."

Tips & gotchas

The skill's effectiveness depends heavily on the clarity and detail of the original code review comments. It is best used with well-written, constructive feedback for optimal results.