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Multi Agent Performance Profiling

Checked
terrylica·vlatest·agents·MIT

Analyzes and optimizes collaborative agent teams' performance via detailed metrics and actionable insights from Terrylica.

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 terrylica/cc-skills --skill multi-agent-performance-profiling -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

terrylica

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 terrylica/cc-skills at 3a1dc29965.

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

Commit
3a1dc29965
Manifest
plugins/quality-tools/skills/multi-agent-performance-profiling/SKILL.md
Scanned
5 files, 1,127 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

This skill analyzes and optimizes the performance of collaborative agent teams within complex workflows. It uses a multi-layer profiling approach involving five parallel agents to identify bottlenecks across various system layers, such as database configuration, client library usage, batch sizes, and overall pipeline stages. The skill quantifies each stage's contribution to total time and prioritizes optimization efforts based on impact.

When to use it

  • When performance is below a defined Service Level Objective (SLO).
  • For optimizing multi-stage pipelines (e.g., download → extract → parse → ingest).
  • To investigate database performance issues.
  • To identify bottlenecks in complex workflows.
  • As a pre-optimization analysis step before making changes to a system.

Key capabilities

  • Multi-Layer Profiling: Uses five parallel agents for comprehensive performance assessment.
  • Phase-Boundary Instrumentation: Measures time using time.perf_counter() at key stages in the workflow.
  • Memory Profiling: Tracks peak memory usage and allocations.
  • Database Configuration Analysis: Reviews server settings and compares production vs. development configurations.
  • Client Library Analysis: Examines API usage patterns and identifies buffer size tuning opportunities.
  • Batch Size Analysis: Determines optimal batch sizes considering memory overhead and throughput.
  • Prioritization: Assigns priorities (P0/P1/P2) to optimization tasks based on impact quantification.

Example prompts

  • "Analyze the performance of our data ingestion pipeline."
  • "Identify bottlenecks in our multi-stage processing workflow."
  • "Profile the database configuration and suggest potential improvements."

Tips & gotchas

  • The skill operates using a parallel execution pattern, with no dependencies between investigation agents (1-4). The integration agent synthesizes findings from all agents.
  • It's designed to identify true bottlenecks rather than relying on assumed causes.
  • Dynamic todo management is used; the skill spawns agents in parallel and updates todos as they complete.