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.
git clone --depth 1 https://github.com/EngineerWithAI/engineerwith-agentsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/engineerwithai/engineerwith-agents/multi-agent-optimize)<a href="https://agentmods.dev/commands/engineerwithai/engineerwith-agents/multi-agent-optimize"><img src="https://agentmods.dev/badge/commands/engineerwithai/engineerwith-agents/multi-agent-optimize/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/engineerwithai/engineerwith-agents/multi-agent-optimize"><img src="https://agentmods.dev/badge/commands/engineerwithai/engineerwith-agents/multi-agent-optimize.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 5.1 | $0.00000 | $0.01078 |
| Opus 5.5 | $0.00000 | $0.00431 |
| Sonnet 5.5 | $0.00000 | $0.00216 |
| Haiku 4.5 | $0.00000 | $0.00108 |
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 1mo 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.
This is a copy
100% identical to multi-agent-optimize — 29 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.
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)
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.
- 1mo ago First seen · 189 lines · 0 tokens per session scan A 4972796ebd4a
multi-agent-optimize is a command published in the GitHub repository EngineerWithAI/engineerwith-agents (4 stars, last pushed 9mo 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 29 lines, and is treated as a copy.
Other commands, from other repositories
qa-changes
This skill should be used when the user asks to "QA a pull request", "test PR changes", "verify a PR works", "functionally test changes", or when an automated workflow triggers QA validation of code changes. Provides a structured methodology for setting up the environment, exercising changed behavior, and reporting…
react-native
Command "react-native" from getsentry/toolkit, covering examples, also associate with a release and distribution(s), xcode build step (react-native xcode) and important notes.
doctor
Diagnosticar y reparar problemas del framework Don Cheli, git y entorno. Usa cuando el usuario dice "doctor", "problemas del framework", "don cheli no funciona", "repair Don Cheli", "debug setup", "setup broken", "framework broken", "reparar entorno". Detecta y repara issues de configuración, git y dependencias…
doctor
Badi configuration validation. Checks all Badi components and produces a diagnostic report.
auditar-cascading
Invoca cascading-failures-auditor — audita serviço para 5 triggers de cascade (sem timeout, retry sem jitter, sem circuit breaker, sem deadline, queue unbounded) e gera CASCADING-AUDIT.md.
http-service
Build, review or debug a Bun HTTP service. Loads the http-service skill, then works the task through its workflow.