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Code Review Excellence

Checked
awesome-skills·vlatest·backend·MIT

Automates thorough code reviews, identifying bugs, security vulnerabilities, and suggesting performance improvements for backend systems.

Repository

Install on your platform

This skill no longer installs under the name code-review-excellence. awesome-skills/code-review-skill has no skill named "code-review-excellence" any more. It was renamed, moved or deleted upstream.

We check each listing against its upstream repository with the rules the skills CLI uses, last on 2026-09-28. We don't show a command we know will fail.

Repository install instructions

What we know about this skill

Publisher

awesome-skills

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 awesome-skills/code-review-skill at 4850184dfd.

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

Commit
4850184dfd
Manifest
SKILL.md
Scanned
48 files, 24,588 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

Code Review Excellence

What it does

This skill enables AI agents to perform high-quality code reviews, analyzing submissions for bugs, security vulnerabilities, and adherence to best practices. It helps teams maintain code integrity by providing structured feedback on logic errors and style consistency before merging changes.

When to use it

  • Before merging pull requests to catch logical errors early in the development cycle.
  • During pair programming sessions to validate architectural decisions in real-time.
  • In CI/CD pipelines to automatically gate deployments based on review quality scores.
  • For onboarding new developers by enforcing consistent coding standards across the team.

Key capabilities

  • Automated detection of common bugs and security flaws in submitted code.
  • Analysis of code style consistency with project guidelines.
  • Generation of actionable feedback reports for developers.
  • Support for multiple programming languages and frameworks.

Example prompts

  • "Review this Python function for potential race conditions and suggest improvements."
  • "Analyze the recent pull request for security vulnerabilities and compliance issues."
  • "Provide a detailed code review focusing on performance bottlenecks in this API endpoint."

Tips & gotchas

Ensure your AI agent has access to the project's coding standards documentation before initiating reviews. Limit large codebase scans to prevent token overflow errors by reviewing files incrementally.