What is multi-agent-optimize?

A command for analyzing and improving an application's performance with several coordinated agents. It accepts a target, performance goals, optimization scope, budget limits, and quality measures.

Use it to profile a system, identify slow areas, compare quick fixes with deeper optimization, and track performance and efficiency goals.

It provides a structured way to find bottlenecks across different parts of a system and weigh improvements against cost and resource limits.

multi-agent-optimize — Command by EngineerWithAI

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods

npx agentmods add commands/engineerwithai/engineerwith-agents/multi-agent-optimize

Clone the repo

git clone --depth 1 https://github.com/EngineerWithAI/engineerwith-agents

Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.

When invoked 1,078 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. Scan, not verified.

Origin 100% copy Near-identical to another mod in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5	$0.00000	$0.01078
Opus 5	$0.00000	$0.00539
Sonnet 5	$0.00000	$0.00216
Haiku 4.5	$0.00000	$0.00108

Measured 2d ago against content hash 4972796ebd4a, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

multi-agent-optimize scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

100% identical to multi-agent-optimize — 23 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

plugins/agent-orchestration/commands/multi-agent-optimize.md · 189 lines

How it starts

The opening of the file, as written. The whole thing — 189 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Multi-Agent Optimization Toolkit

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

Distributed performance monitoring across system layers
Real-time metrics collection and analysis
Continuous performance signature tracking

Profiling Agents

Database Performance Agent
- Query execution time analysis
- Index utilization tracking
- Resource consumption monitoring
Application Performance Agent
- CPU and memory profiling
- Algorithmic complexity assessment
- Concurrency and async operation analysis
Frontend Performance Agent
- Rendering performance metrics
- Network request optimization
- Core Web Vitals monitoring

Profiling Code Example

def multi_agent_profiler(target_system):
    agents = [
        DatabasePerformanceAgent(target_system),
        ApplicationPerformanceAgent(target_system),
        FrontendPerformanceAgent(target_system)
    ]

    performance_profile = {}
    for agent in agents:
        performance_profile[agent.__class__.__name__] = agent.profile()

    return aggregate_performance_metrics(performance_profile)

Read the full file on GitHub · 189 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

2d ago First seen · 189 lines · 0 tokens per session scan A 4972796ebd4a

Subscribe to this mod's changes

multi-agent-optimize is a command published in the GitHub repository EngineerWithAI/engineerwith-agents (4 stars, last pushed 7mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,078 tokens. A static security scan graded it A with 0 findings. It is 100% identical to multi-agent-optimize, differing in 23 lines, and is treated as a copy.

Analytics and metrics

multi-agent-optimize