Antigravity Skill Vault is a collection of reusable Agent Skills for Google Antigravity, covering software development, operations, security, and business work. It is for people who want Antigravity agents to follow specialized expertise, personas, and structured workflows. The catalogue skills are entries from this collection.
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 rmyndharis/antigravity-skills --skill agent-orchestration-multi-agent-optimizegit clone --depth 1 https://github.com/rmyndharis/antigravity-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/rmyndharis/antigravity-skills/agent-orchestration-multi-agent-optimize)<a href="https://agentmods.dev/skills/rmyndharis/antigravity-skills/agent-orchestration-multi-agent-optimize"><img src="https://agentmods.dev/badge/skills/rmyndharis/antigravity-skills/agent-orchestration-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/skills/rmyndharis/antigravity-skills/agent-orchestration-multi-agent-optimize"><img src="https://agentmods.dev/badge/skills/rmyndharis/antigravity-skills/agent-orchestration-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 | $0.00038 | $0.01300 |
| Opus 5 | $0.00019 | $0.00650 |
| Sonnet 5 | $0.00008 | $0.00260 |
| Haiku 4.5 | $0.00004 | $0.00130 |
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 10d 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
94% identical to agent-orchestration-multi-agent-optimize — 14 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 — 240 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.
- 10d ago First seen · 240 lines · 38 tokens per session scan A 49bc1053b8b1
agent-orchestration-multi-agent-optimize is a skill published in the GitHub repository rmyndharis/antigravity-skills (1,517 stars, last pushed 1mo ago), licensed MIT. It adds 38 tokens to every session and 1,300 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to agent-orchestration-multi-agent-optimize, differing in 14 lines, and is treated as a copy.
Other skills, from other repositories
code-review
Systematic code review skill covering both requesting a review (pre-commit checklist) and receiving and responding to review feedback. Checks code quality, security, test coverage, architectural alignment, and documentation before any code is committed.
test-driven-execution
Before writing any implementation code, define the acceptance criteria and test cases that the code must satisfy. Agents then write code to pass these tests — not to match a vague description. Eliminates "it works on my machine" and "I think this is what you wanted" outcomes.
writing-plans
TÜRKÇE AÇIKLAMA ─────────────── Bu skill, onaylanmış bir scope veya fikirden somut, uygulanabilir bir implementasyon planı üretir. Hangi dosya değişecek, hangi sırayla, kim yapacak, ne kadar sürecek, hangi riskler var — hepsini netleştirir. Planın çıktısı doğrudan executing-plans veya dispatching-parallel-agents…
idea-validator
Structured validation framework that scores product ideas. Use when evaluating problem severity, willingness-to-pay, or founder-market fit. For market intelligence, see market-research.
brainstorming
TÜRKÇE AÇIKLAMA ─────────────── Bu skill, bir fikir veya problemi Sokratik yöntemle rafine eder. Agent sana cevap vermez — sorular sorar. Bu sorular aracılığıyla fikrin netleşir, varsayımlar sorgulanır, kapsam belirlenir ve gerçek ihtiyaç ortaya çıkar. "Ne yapalım?" sorusunu "Tam olarak ne yapmamız gerekiyor ve…
project-context-primer
Run this skill at the very start of any new conversation or agent session before writing a single line of code. It loads the project's architectural decisions, conventions, known gotchas, and current task status so the agent operates with full context — not as a blank slate.