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 skills add diegosouzapw/awesome-omni-skills --skill agent-orchestration-multi-agent-optimize-v2git clone --depth 1 https://github.com/diegosouzapw/awesome-omni-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/diegosouzapw/awesome-omni-skills/agent-orchestration-multi-agent-optimize-v2)<a href="https://agentmods.dev/skills/diegosouzapw/awesome-omni-skills/agent-orchestration-multi-agent-optimize-v2"><img src="https://agentmods.dev/badge/skills/diegosouzapw/awesome-omni-skills/agent-orchestration-multi-agent-optimize-v2/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/skills/diegosouzapw/awesome-omni-skills/agent-orchestration-multi-agent-optimize-v2"><img src="https://agentmods.dev/badge/skills/diegosouzapw/awesome-omni-skills/agent-orchestration-multi-agent-optimize-v2.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00074 | $0.03043 |
| Opus 5 | $0.00037 | $0.01522 |
| Sonnet 5 | $0.00015 | $0.00609 |
| Haiku 4.5 | $0.00007 | $0.00304 |
Grade A, and why
agent-orchestration-multi-agent-optimize-v2 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 11d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- agent-orchestration-multi-agent-optimize — 97% identical, 18 lines differ
How it starts
The opening of the file, as written. The whole thing — 381 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Multi-Agent Optimization Toolkit
Overview
This public intake copy packages plugins/antigravity-awesome-skills/skills/agent-orchestration-multi-agent-optimize from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.
Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.
This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.
Multi-Agent Optimization Toolkit
Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Safety, Role: AI-Powered Multi-Agent Performance Engineering Specialist, Arguments Handling, 1. Multi-Agent Performance Profiling, 2. Context Window Optimization, 3. Agent Coordination Efficiency.
When to Use This Skill
Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.
- 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
- You only need to tune a single agent prompt
- There are no measurable metrics or evaluation data
Operating Table
| Situation | Start here | Why it matters |
|---|---|---|
| First-time use | metadata.json |
Confirms repository, branch, commit, and imported path through the external_source block before touching the copied workflow |
| Provenance review | ORIGIN.md |
Gives reviewers a plain-language audit trail for the imported source |
| Workflow execution | SKILL.md |
Starts with the smallest copied file that materially changes execution |
| Supporting context | SKILL.md |
Adds the next most relevant copied source file without loading the entire package |
| Handoff decision | ## Related Skills |
Helps the operator switch to a stronger native skill when the task drifts |
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 381 lines · 74 tokens per session scan A 9a1a6f1c694f
agent-orchestration-multi-agent-optimize-v2 is a skill published in the GitHub repository diegosouzapw/awesome-omni-skills (140 stars, last pushed 2mo ago), licensed MIT. It adds 74 tokens to every session and 3,043 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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