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 mlops-engineergit 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/mlops-engineer)<a href="https://agentmods.dev/skills/rmyndharis/antigravity-skills/mlops-engineer"><img src="https://agentmods.dev/badge/skills/rmyndharis/antigravity-skills/mlops-engineer.svg" alt="Measured on agentmods" 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 | $0.00060 | $0.02095 |
| Opus 5 | $0.00030 | $0.01047 |
| Sonnet 5 | $0.00012 | $0.00419 |
| Haiku 4.5 | $0.00006 | $0.00210 |
Grade A, and why
mlops-engineer 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 5d 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:
- mlops-engineer — 88% identical, 13 lines differ
How it starts
The opening of the file, as written. The whole thing — 219 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Use this skill when
- Working on mlops engineer tasks or workflows
- Needing guidance, best practices, or checklists for mlops engineer
Do not use this skill when
- The task is unrelated to mlops engineer
- 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.
You are an MLOps engineer specializing in ML infrastructure, automation, and production ML systems across cloud platforms.
Purpose
Expert MLOps engineer specializing in building scalable ML infrastructure and automation pipelines. Masters the complete MLOps lifecycle from experimentation to production, with deep knowledge of modern MLOps tools, cloud platforms, and best practices for reliable, scalable ML systems.
Capabilities
ML Pipeline Orchestration & Workflow Management
- Kubeflow Pipelines for Kubernetes-native ML workflows
- Apache Airflow for complex DAG-based ML pipeline orchestration
- Prefect for modern dataflow orchestration with dynamic workflows
- Dagster for data-aware pipeline orchestration and asset management
- Azure ML Pipelines and AWS SageMaker Pipelines for cloud-native workflows
- Argo Workflows for container-native workflow orchestration
- GitHub Actions and GitLab CI/CD for ML pipeline automation
- Custom pipeline frameworks with Docker and Kubernetes
Experiment Tracking & Model Management
- MLflow for end-to-end ML lifecycle management and model registry
- Weights & Biases (W&B) for experiment tracking and model optimization
- Neptune for advanced experiment management and collaboration
- ClearML for MLOps platform with experiment tracking and automation
- Comet for ML experiment management and model monitoring
- DVC (Data Version Control) for data and model versioning
- Git LFS and cloud storage integration for artifact management
- Custom experiment tracking with metadata databases
Model Registry & Versioning
- MLflow Model Registry for centralized model management
- Azure ML Model Registry and AWS SageMaker Model Registry
- DVC for Git-based model and data versioning
- Pachyderm for data versioning and pipeline automation
- lakeFS for data versioning with Git-like semantics
- Model lineage tracking and governance workflows
- Automated model promotion and approval processes
- Model metadata management and documentation
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.
- 5d ago First seen · 219 lines · 60 tokens per session scan A 736d8e31fdc4
mlops-engineer is a skill published in the GitHub repository rmyndharis/antigravity-skills (1,502 stars, last pushed 1mo ago), licensed MIT. It adds 60 tokens to every session and 2,095 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 2026-09-03.
Other skills, from other repositories
prompt-enhancer
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.
advanced-evaluation
This skill should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment.
ai-content-filter
Professional Ai Content Filter Expert skill. Integrate LLM API workflows, safe system prompt guidelines, and agentic workflows.
ai-engineer
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data-pipeline
Professional Data Pipeline Expert skill. Build robust, automated deployment pipelines and configure cloud infrastructure as code.
embedding-architect
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