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Agent Code Review Swarm

Checked
ruvnet·vlatest·agents·MIT

Ruvnet's agent-code-review-swarm automatically identifies bugs, security flaws, and style issues in code via collaborative AI review.

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 ruvnet/ruflo --skill agent-code-review-swarm --full-depth -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

ruvnet

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 ruvnet/claude-flow at 49da6ecc44.

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

Commit
49da6ecc44
Manifest
.agents/skills/agent-code-review-swarm/SKILL.md
Scanned
1 file, 487 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 agent-code-review-swarm skill orchestrates a swarm of AI agents to perform thorough code reviews. It leverages multiple agents, each specializing in different aspects of code quality (e.g., security, style, performance), and aggregates their findings into a consolidated report. This allows for more comprehensive feedback than a single agent could provide.

When to use it

  • Large Codebase Reviews: When dealing with substantial projects where multiple perspectives are valuable.
  • Security Audits: To identify potential vulnerabilities across various code segments.
  • Style Guide Enforcement: Ensure consistent coding standards and best practices throughout a project.
  • Pre-Merge Checks: Automate code review processes before integrating changes into the main codebase.

Key capabilities

  • Multi-agent orchestration for comprehensive reviews
  • Specialized agent roles (security, style, performance)
  • Consolidated reporting of findings from multiple agents

Example prompts

  • "Review this Python script for security vulnerabilities: [script content]"
  • "Perform a code review on this JavaScript file, focusing on adherence to the Airbnb style guide: [file content]"
  • "Can you analyze this Go program and identify potential performance bottlenecks? [program content]"

Tips & gotchas

The quality of the review depends heavily on the underlying agents' capabilities. Ensure the agents used are well-suited for the specific programming language and code domain being reviewed.