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jarvis

Orchestrate autonomous AI research teams for performance optimization tasks.

Updated Aug 23, 2026
Search Tags:#ai#autonomous agents#performance optimization#multi-agent systems#fleet orchestration#research management
One-click install
npx skills add https://github.com/SteeZyT33/perf-lab-plugin --skill jarvis
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: jarvis
Source: https://github.com/SteeZyT33/perf-lab-plugin/tree/main/skills/jarvis
Command: npx skills add https://github.com/SteeZyT33/perf-lab-plugin --skill jarvis

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill manages and coordinates multiple autonomous AI research teams, automating complex optimization tasks by orchestrating a fleet of specialized agents.

Core Features & Use Cases

  • Fleet Orchestration: Launches, monitors, and manages multiple research teams running in parallel.
  • Autonomous Iteration: Facilitates continuous experimentation and learning across teams.
  • Knowledge Curation: Consolidates research findings into human-readable logs and documentation.
  • Use Case: Deploying a fleet of AI agents to optimize a complex software performance metric, where each team explores different optimization strategies, shares breakthroughs, and builds institutional knowledge.

Quick Start

Launch five research teams to begin optimizing the project's performance metric.

Frequently Asked Questions about jarvis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I orchestrate a fleet of autonomous AI research agents for performance optimization?▼

Multi-agent fleet orchestration is managed by launching parallel research teams to explore different optimization strategies, share breakthroughs, and build institutional knowledge for complex software performance tasks.

What is the best way to manage multiple AI research teams running in parallel?▼

Managing parallel AI research teams requires tracking team lifecycles, monitoring agent health, and curating knowledge into human-readable logs to consolidate findings from continuous autonomous experimentation.

Do I need tmux to manage autonomous multi-agent systems for software optimization?▼

Yes, tmux is required for session management alongside bash scripts for inter-agent communication and task execution to properly launch, monitor, and teardown autonomous research teams.

Can I expand a running fleet of autonomous AI agents dynamically?▼

Yes, the fleet supports launching, status reporting, expansion, and graceful teardown of research teams, allowing you to dynamically scale autonomous agents during continuous performance optimization tasks.

How does knowledge curation work across multiple AI research teams?▼

Knowledge curation consolidates research findings and breakthroughs relayed across teams into human-readable logs and documentation, building institutional knowledge from continuous autonomous experimentation.

What are the limitations of using bash scripts for multi-agent fleet orchestration?▼

Fleet orchestration via bash scripts and tmux is constrained by terminal session management capabilities, meaning inter-agent communication and task execution depend entirely on local shell environments.

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