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Github Pr Review

Checked
fvadicamo·vlatest·backend·MIT

Helps with GitHub, code review as part of agent workflows 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 fvadicamo/dev-agent-skills --skill github-pr-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

fvadicamo

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 fvadicamo/dev-agent-skills at 8e710c57b4.

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

Commit
8e710c57b4
Manifest
plugins/github-workflow/skills/github-pr-review/SKILL.md
Scanned
3 files, 611 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

The github-pr-review skill enables AI agents to analyze and provide feedback on GitHub pull requests. It can review code changes, identify potential issues, suggest improvements, and generate concise summaries of the PR's purpose and impact.

When to use it

  • To automate initial code reviews for pull requests in a development workflow
  • When you need quick insights into the quality and clarity of proposed changes
  • For teams looking to streamline their CI/CD pipeline with AI-assisted feedback

Key capabilities

  • Analyzes GitHub pull request diffs
  • Identifies potential bugs or style issues
  • Suggests code improvements and optimizations
  • Summarizes the purpose and scope of a PR

Example prompts

  • "Review this GitHub pull request and highlight any potential issues."
  • "Provide feedback on the changes in this PR, focusing on code quality and best practices."
  • "Summarize the main changes in this GitHub pull request for the team."

Tips & gotchas

  • Ensure that the AI agent has access to the repository and proper authentication credentials to fetch PR details.
  • This skill works best with clear, well-documented changes; ambiguous or overly complex diffs may lead to less accurate feedback.