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 machine-learning-ops-ml-pipelinegit 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/machine-learning-ops-ml-pipeline)<a href="https://agentmods.dev/skills/rmyndharis/antigravity-skills/machine-learning-ops-ml-pipeline"><img src="https://agentmods.dev/badge/skills/rmyndharis/antigravity-skills/machine-learning-ops-ml-pipeline/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/machine-learning-ops-ml-pipeline"><img src="https://agentmods.dev/badge/skills/rmyndharis/antigravity-skills/machine-learning-ops-ml-pipeline.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 | $0.00022 | $0.02215 |
| Opus 5 | $0.00011 | $0.01107 |
| Sonnet 5 | $0.00004 | $0.00443 |
| Haiku 4.5 | $0.00002 | $0.00221 |
Grade A, and why
machine-learning-ops-ml-pipeline 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 9d 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.
How it starts
The opening of the file, as written. The whole thing — 314 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Machine Learning Pipeline - Multi-Agent MLOps Orchestration
Design and implement a complete ML pipeline for: $ARGUMENTS
Use this skill when
- Working on machine learning pipeline - multi-agent mlops orchestration tasks or workflows
- Needing guidance, best practices, or checklists for machine learning pipeline - multi-agent mlops orchestration
Do not use this skill when
- The task is unrelated to machine learning pipeline - multi-agent mlops orchestration
- You need a different domain or tool outside this scope
Instructions
- Clarify goals, constraints, and required inputs.
- Apply relevant best practices and validate outcomes.
- Provide actionable steps and verification.
Thinking
This workflow orchestrates multiple specialized agents to build a production-ready ML pipeline following modern MLOps best practices. The approach emphasizes:
- Phase-based coordination: Each phase builds upon previous outputs, with clear handoffs between agents
- Modern tooling integration: MLflow/W&B for experiments, Feast/Tecton for features, KServe/Seldon for serving
- Production-first mindset: Every component designed for scale, monitoring, and reliability
- Reproducibility: Version control for data, models, and infrastructure
- Continuous improvement: Automated retraining, A/B testing, and drift detection
The multi-agent approach ensures each aspect is handled by domain experts:
- Data engineers handle ingestion and quality
- Data scientists design features and experiments
- ML engineers implement training pipelines
- MLOps engineers handle production deployment
- Observability engineers ensure monitoring
Phase 1: Data & Requirements Analysis
Deliverables:
- Data source audit and ingestion strategy:
- Source systems and connection patterns
- Schema validation using Pydantic/Great Expectations
- Data versioning with DVC or lakeFS
- Incremental loading and CDC strategies
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.
- 9d ago First seen · 314 lines · 22 tokens per session scan A f6515e90658d
machine-learning-ops-ml-pipeline is a skill published in the GitHub repository rmyndharis/antigravity-skills (1,529 stars, last pushed 1mo ago), licensed MIT. It adds 22 tokens to every session and 2,215 once invoked, about $0.0001 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 2026-09-03.
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TÜRKÇE AÇIKLAMA ─────────────── Bu skill, belirsiz veya eksik bir kullanıcı isteğini alır ve onu daha net, daha spesifik, daha uygulanabilir bir prompt'a dönüştürür. Agent'ın yanlış anlayarak iş yapmasını önler. "Şunu düzelt" gibi muğlak bir istek, "hangi koşulda ne bekleniyor, başarı kriteri ne" formatına çevrilir.
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