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Code Review Quality

Checked
proffesor-for-testing·vlatest·backend·MIT

Analyzes backend code for potential bugs, performance bottlenecks, and adherence to style guidelines, suggesting improvements.

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 proffesor-for-testing/agentic-qe --skill code-review-quality -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

proffesor-for-testing

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 proffesor-for-testing/agentic-qe at 124d8c735e.

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

Commit
124d8c735e
Manifest
assets/skills/code-review-quality/SKILL.md
Scanned
6 files, 1,066 lines
Copies in repo
2 — all of them scanned
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 enables AI agents to perform automated code reviews, identifying potential bugs, security vulnerabilities, and style inconsistencies within source code. It provides immediate feedback on pull requests to maintain high standards of software quality before deployment.

When to use it

  • Integrating into CI/CD pipelines to block merges containing critical errors.
  • Enforcing consistent coding conventions across large, distributed development teams.
  • Conducting rapid security audits on newly submitted backend modules.
  • Reducing manual review time for junior developers by highlighting obvious logic flaws.

Key capabilities

  • Automated syntax and style validation against project standards.
  • Detection of common runtime errors and memory leaks.
  • Security vulnerability scanning within code submissions.
  • Context-aware suggestions for refactoring complex functions.

Example prompts

  • "Review this pull request and list any security vulnerabilities or logic errors you find."
  • "Analyze the new authentication module for potential race conditions and suggest fixes."
  • "Check if this API endpoint implementation follows our team's backend coding style guide."

Tips & gotchas

Ensure the AI agent has access to the full project context, including existing architecture patterns, to provide accurate refactoring suggestions. While effective at catching standard issues, complex architectural decisions may still require human oversight.