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Frontend Code Review

Checked
pageai-pro·vlatest·backend·MIT

Helps with frontend development, code review as part of building frontend UIs and user experiences workflows.

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 pageai-pro/ralph-loop --skill frontend-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

pageai-pro

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 pageai-pro/ralph-loop at eba65ebbb0.

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

Scanned
3 files, 130 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 for frontend projects. It analyzes provided code snippets, identifies potential issues related to style, best practices, and common errors, and offers suggestions for improvement. The tool aims to streamline the development process by catching problems early and ensuring consistent code quality.

When to use it

  • During feature development: Integrate into your workflow to get immediate feedback on new code contributions.
  • Before pull requests: Run a review before submitting code for team approval, reducing review time.
  • For onboarding new developers: Help ensure consistency and adherence to coding standards within the team.
  • Refactoring existing code: Identify areas where improvements can be made in legacy codebase.

Key capabilities

  • Automated code analysis
  • Style guide enforcement
  • Error detection
  • Suggestion generation

Example prompts

  • "Review this React component for potential issues: [code snippet]"
  • "Can you check this JavaScript function for adherence to best practices? [code snippet]"
  • "Analyze this Vue.js template and suggest improvements."

Tips & gotchas

The quality of the review depends on the clarity and completeness of the provided code. Ensure that any necessary context or dependencies are included when submitting code for analysis.