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Performance Analysis

Checked
ruvnet·vlatest·agents·MIT

Helps with performance optimization, analysis as part of agent workflows workflows.

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 performance-analysis --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/performance-analysis/SKILL.md
Scanned
2 files, 1,057 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 provides a comprehensive suite of tools for performance analysis within agent workflows. It enables AI agents to identify bottlenecks in communication, processing, memory, and network usage. The skill also facilitates real-time monitoring, historical analysis of swarm operations, automated report generation, and offers AI-powered recommendations for optimizing performance.

When to use it

  • When you need to pinpoint the cause of slow or inefficient agent workflows.
  • To generate detailed reports on agent performance over specific time periods (e.g., 1 hour, 24 hours, 7 days).
  • For automatically identifying and attempting to fix performance bottlenecks within a swarm.
  • When you need to analyze the performance of a particular swarm using its ID.

Key capabilities

  • Bottleneck Detection: Identifies performance bottlenecks across communication, processing, memory, and network.
  • Performance Profiling: Provides real-time monitoring and historical analysis of swarm operations.
  • Report Generation: Creates comprehensive performance reports in multiple formats (e.g., HTML).
  • Optimization Recommendations: Offers AI-powered suggestions for improving performance.
  • Automatic Fixes: Can apply automatic optimizations to detected bottlenecks based on a defined threshold.

Example prompts

  • "Run a basic bottleneck detection analysis."
  • "Generate a performance report in HTML format, including metrics, covering the last 24 hours."
  • "Detect and automatically fix any bottlenecks with a severity above 15% within swarm 'swarm-123'."

Tips & gotchas

  • The skill requires claude-flow to be installed.
  • You can specify a time range for analysis (e.g., 1h, 24h, 7d, all).
  • Adjust the threshold percentage (--threshold) to control the sensitivity of bottleneck detection; lower values will identify more issues but may also include less critical ones.