# Agent Orchestration Multi Agent Optimize

Optimize multi-agent systems with coordinated profiling, workload distribution, and...

- Repository: https://github.com/rmyndharis/antigravity-skills (1913 stars)
- Category: AI and agents
- License: MIT
- Last updated: 2026-10-14
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add rmyndharis/antigravity-skills --skill agent-orchestration-multi-agent-optimize -g -a claude-code -y`
Codex: `npx skills add rmyndharis/antigravity-skills --skill agent-orchestration-multi-agent-optimize -g -a codex -y`
Cursor: `npx skills add rmyndharis/antigravity-skills --skill agent-orchestration-multi-agent-optimize -g -a cursor -y`
Gemini CLI: `npx skills add rmyndharis/antigravity-skills --skill agent-orchestration-multi-agent-optimize -g -a gemini-cli -y`

## Description

Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Multi-Agent Optimization Toolkit

## Use this skill when

- Improving multi-agent coordination, throughput, or latency
- Profiling agent workflows to identify bottlenecks
- Designing orchestration strategies for complex workflows
- Optimizing cost, context usage, or tool efficiency

## Do not use this skill when

- You only need to tune a single agent prompt
- There are no measurable metrics or evaluation data
- The task is unrelated to multi-agent orchestration

## Instructions

1. Establish baseline metrics and target performance goals.
2. Profile agent workloads and identify coordination bottlenecks.
3. Apply orchestration changes and cost controls incrementally.
4. Validate improvements with repeatable tests and rollbacks.

## Safety

- Avoid deploying orchestration changes without regression testing.
- Roll out changes gradually to prevent system-wide regressions.

## Role: AI-Powered Multi-Agent Performance Engineering Specialist

### Context

The Multi-Agent Optimization Tool is an advanced AI-driven framework designed to holistically improve system performance through intelligent, coordinated agent-based optimization. Leveraging cutting-edge AI orchestration techniques, this tool provides a comprehensive approach to performance engineering across multiple domains.

### Core Capabilities

- Intelligent multi-agent coordination
- Performance profiling and bottleneck identification
- Adaptive optimization strategies
- Cross-domain performance optimization
- Cost and efficiency tracking

## Arguments Handling

The tool processes optimization arguments with flexible input parameters:

- `$TARGET`: Primary system/application to optimize
- `$PERFORMANCE_GOALS`: Specific performance metrics and objectives
- `$OPTIMIZATION_SCOPE`: Depth of optimization (quick-win, comprehensive)
- `$BUDGET_CONSTRAINTS`: Cost and resource limitations
- `$QUALITY_METRICS`: Performance quality thresholds

## 1. Multi-Agent Performance Profiling

### Profiling Strategy

The rest: https://github.com/rmyndharis/antigravity-skills/blob/HEAD/skills/agent-orchestration-multi-agent-optimize/SKILL.md