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 ml-pipeline-workflowgit 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/ml-pipeline-workflow)<a href="https://agentmods.dev/skills/rmyndharis/antigravity-skills/ml-pipeline-workflow"><img src="https://agentmods.dev/badge/skills/rmyndharis/antigravity-skills/ml-pipeline-workflow/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/ml-pipeline-workflow"><img src="https://agentmods.dev/badge/skills/rmyndharis/antigravity-skills/ml-pipeline-workflow.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 | $0.00048 | $0.01538 |
| Opus 5 | $0.00024 | $0.00769 |
| Sonnet 5 | $0.00010 | $0.00308 |
| Haiku 4.5 | $0.00005 | $0.00154 |
Grade A, and why
ml-pipeline-workflow 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.
This is a copy
91% identical to ml-pipeline-workflow — 13 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 — 257 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ML Pipeline Workflow
Complete end-to-end MLOps pipeline orchestration from data preparation through model deployment.
Do not use this skill when
- The task is unrelated to ml pipeline workflow
- 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.
Overview
This skill provides comprehensive guidance for building production ML pipelines that handle the full lifecycle: data ingestion → preparation → training → validation → deployment → monitoring.
Use this skill when
- Building new ML pipelines from scratch
- Designing workflow orchestration for ML systems
- Implementing data → model → deployment automation
- Setting up reproducible training workflows
- Creating DAG-based ML orchestration
- Integrating ML components into production systems
What This Skill Provides
Core Capabilities
-
Pipeline Architecture
- End-to-end workflow design
- DAG orchestration patterns (Airflow, Dagster, Kubeflow)
- Component dependencies and data flow
- Error handling and retry strategies
-
Data Preparation
- Data validation and quality checks
- Feature engineering pipelines
- Data versioning and lineage
- Train/validation/test splitting strategies
-
Model Training
- Training job orchestration
- Hyperparameter management
- Experiment tracking integration
- Distributed training patterns
-
Model Validation
- Validation frameworks and metrics
- A/B testing infrastructure
- Performance regression detection
- Model comparison workflows
-
Deployment Automation
- Model serving patterns
- Canary deployments
- Blue-green deployment strategies
- Rollback mechanisms
Reference Documentation
See the references/ directory for detailed guides:
- data-preparation.md - Data cleaning, validation, and feature engineering
- model-training.md - Training workflows and best practices
- model-validation.md - Validation strategies and metrics
- model-deployment.md - Deployment patterns and serving architectures
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 · 257 lines · 48 tokens per session scan A cc9d1062ed8b
ml-pipeline-workflow is a skill published in the GitHub repository rmyndharis/antigravity-skills (1,529 stars, last pushed 1mo ago), licensed MIT. It adds 48 tokens to every session and 1,538 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to ml-pipeline-workflow, differing in 13 lines, and is treated as a copy.
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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