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.
npx agentmods add commands/hermeticormus/claude-code-game-development/multi-agent-optimizegit clone --depth 1 https://github.com/HermeticOrmus/claude-code-game-developmentWhat 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 |
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.
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.
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.
- 2d ago First seen · 189 lines · 0 tokens per session scan A 4972796ebd4a
multi-agent-optimize is a command published in the GitHub repository HermeticOrmus/claude-code-game-development (58 stars, last pushed 3mo 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.
Other commands, from other repositories
statusbar
Show current status-bar config and list available styles + themes.
audio-pass
Functional audio review for implementation, mix coverage, clarity, and player feedback.
liveops-brief
Create a concise live operations brief for an event, offer, patch, or campaign.
context
Activate a project phase context and align agents, commands, and priorities to it.
ui-flow-review
Review menus, HUD, navigation, and player flow from a UX perspective.
adopt
Brownfield onboarding audit and migration plan.