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Agent Reviewer

Checked
ruvnet·vlatest·agents·MIT

Ruvnet's agent-reviewer analyzes agent conversations, identifying areas for improvement in response quality and adherence to guidelines.

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-reviewer --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-reviewer/SKILL.md
Scanned
1 file, 313 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-reviewer skill provides automated feedback on AI agent performance. It analyzes agent outputs based on defined criteria, identifying areas of strength and weakness. This allows for iterative improvement of agents through data-driven insights and targeted adjustments to prompts or underlying models.

When to use it

  • Evaluating new agent versions: Quickly assess the impact of changes made to an agent's prompt or configuration.
  • Identifying common failure modes: Pinpoint recurring errors or weaknesses in an agent’s responses across a range of tasks.
  • Benchmarking agents against each other: Compare the performance of different agent setups on standardized evaluation datasets.
  • Automating quality assurance: Integrate into a continuous integration/continuous delivery (CI/CD) pipeline to ensure consistent agent quality.

Key capabilities

  • Automated feedback generation
  • Performance analysis based on defined criteria
  • Identification of strengths and weaknesses in agent outputs
  • Comparison of different agent versions or configurations

Example prompts

  • "Review this agent's response: [agent output] against the following criteria: accuracy, completeness, and clarity."
  • "Compare the performance of Agent A and Agent B on this task: [task description]."
  • "Analyze this set of agent responses for common errors related to factual recall."

Tips & gotchas

The skill's effectiveness depends heavily on clearly defined evaluation criteria. Ensure these are specific, measurable, achievable, relevant, and time-bound (SMART) to get the most accurate and actionable feedback.