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-improve-agent-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-improve-agent-v2)<a href="https://agentmods.dev/skills/diegosouzapw/awesome-omni-skills/agent-orchestration-improve-agent-v2"><img src="https://agentmods.dev/badge/skills/diegosouzapw/awesome-omni-skills/agent-orchestration-improve-agent-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-improve-agent-v2"><img src="https://agentmods.dev/badge/skills/diegosouzapw/awesome-omni-skills/agent-orchestration-improve-agent-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 | $0.00061 | $0.03863 |
| Opus 5 | $0.00030 | $0.01931 |
| Sonnet 5 | $0.00012 | $0.00773 |
| Haiku 4.5 | $0.00006 | $0.00386 |
Grade A, and why
agent-orchestration-improve-agent-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 13d 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-improve-agent — 98% identical, 18 lines differ
How it starts
The opening of the file, as written. The whole thing — 487 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Performance Optimization Workflow
Overview
This public intake copy packages plugins/antigravity-awesome-skills/skills/agent-orchestration-improve-agent 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.
Agent Performance Optimization Workflow Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration. [Extended thinking: Agent optimization requires a data-driven approach combining performance metrics, user feedback analysis, and advanced prompt engineering techniques. Success depends on systematic evaluation, targeted improvements, and rigorous testing with rollback capabilities for production safety.]
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, Phase 1: Performance Analysis and Baseline Metrics, Phase 2: Prompt Engineering Improvements, Phase 3: Testing and Validation, Phase 4: Version Control and Deployment, Success Criteria.
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 an existing agent's performance or reliability
- Analyzing failure modes, prompt quality, or tool usage
- Running structured A/B tests or evaluation suites
- Designing iterative optimization workflows for agents
- You are building a brand-new agent from scratch
- There are no metrics, feedback, or test cases available
Operating Table
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.
- 13d ago First seen · 487 lines · 61 tokens per session scan A 40560da17145
agent-orchestration-improve-agent-v2 is a skill published in the GitHub repository diegosouzapw/awesome-omni-skills (140 stars, last pushed 2mo ago), licensed MIT. It adds 61 tokens to every session and 3,863 once invoked, about $0.0003 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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