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 skills/satnamrsm/https-github.com-sickn33-antigravity-awesome-skills/agent-orchestration-multi-agent-optimizenpx skills add satnamrsm/https-github.com-sickn33-antigravity-awesome-skills --skill agent-orchestration-multi-agent-optimizegit clone --depth 1 https://github.com/satnamrsm/https-github.com-sickn33-antigravity-awesome-skillsWrote 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/skills/satnamrsm/https-github.com-sickn33-antigravity-awesome-skills/agent-orchestration-multi-agent-optimize)<a href="https://agentmods.dev/skills/satnamrsm/https-github.com-sickn33-antigravity-awesome-skills/agent-orchestration-multi-agent-optimize"><img src="https://agentmods.dev/badge/skills/satnamrsm/https-github.com-sickn33-antigravity-awesome-skills/agent-orchestration-multi-agent-optimize.svg" alt="Measured on agentmods" 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 | $0.00038 | $0.01306 |
| Opus 5 | $0.00019 | $0.00653 |
| Sonnet 5 | $0.00008 | $0.00261 |
| Haiku 4.5 | $0.00004 | $0.00131 |
Grade A, and why
agent-orchestration-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 6d 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
92% identical to agent-orchestration-multi-agent-optimize — 7 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 — 243 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
- Establish baseline metrics and target performance goals.
- Profile agent workloads and identify coordination bottlenecks.
- Apply orchestration changes and cost controls incrementally.
- 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
- Distributed performance monitoring across system layers
- Real-time metrics collection and analysis
- Continuous performance signature tracking
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.
- 6d ago First seen · 243 lines · 38 tokens per session scan A aa0748a67cb5
agent-orchestration-multi-agent-optimize is a skill published in the GitHub repository satnamrsm/https-github.com-sickn33-antigravity-awesome-skills (5 stars, last pushed yesterday), licensed MIT. It adds 38 tokens to every session and 1,306 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to agent-orchestration-multi-agent-optimize, differing in 7 lines, and is treated as a copy.
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