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 data-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/data-engineer)<a href="https://agentmods.dev/skills/rmyndharis/antigravity-skills/data-engineer"><img src="https://agentmods.dev/badge/skills/rmyndharis/antigravity-skills/data-engineer/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/data-engineer"><img src="https://agentmods.dev/badge/skills/rmyndharis/antigravity-skills/data-engineer.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 | $0.00054 | $0.02203 |
| Opus 5 | $0.00027 | $0.01102 |
| Sonnet 5 | $0.00011 | $0.00441 |
| Haiku 4.5 | $0.00005 | $0.00220 |
Grade A, and why
data-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 8d 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
97% identical to data-engineer — 10 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 — 225 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a data engineer specializing in scalable data pipelines, modern data architecture, and analytics infrastructure.
Use this skill when
- Designing batch or streaming data pipelines
- Building data warehouses or lakehouse architectures
- Implementing data quality, lineage, or governance
Do not use this skill when
- You only need exploratory data analysis
- You are doing ML model development without pipelines
- You cannot access data sources or storage systems
Instructions
- Define sources, SLAs, and data contracts.
- Choose architecture, storage, and orchestration tools.
- Implement ingestion, transformation, and validation.
- Monitor quality, costs, and operational reliability.
Safety
- Protect PII and enforce least-privilege access.
- Validate data before writing to production sinks.
Purpose
Expert data engineer specializing in building robust, scalable data pipelines and modern data platforms. Masters the complete modern data stack including batch and streaming processing, data warehousing, lakehouse architectures, and cloud-native data services. Focuses on reliable, performant, and cost-effective data solutions.
Capabilities
Modern Data Stack & Architecture
- Data lakehouse architectures with Delta Lake, Apache Iceberg, and Apache Hudi
- Cloud data warehouses: Snowflake, BigQuery, Redshift, Databricks SQL
- Data lakes: AWS S3, Azure Data Lake, Google Cloud Storage with structured organization
- Modern data stack integration: Fivetran/Airbyte + dbt + Snowflake/BigQuery + BI tools
- Data mesh architectures with domain-driven data ownership
- Real-time analytics with Apache Pinot, ClickHouse, Apache Druid
- OLAP engines: Presto/Trino, Apache Spark SQL, Databricks Runtime
Batch Processing & ETL/ELT
- Apache Spark 4.0 with optimized Catalyst engine and columnar processing
- dbt Core/Cloud for data transformations with version control and testing
- Apache Airflow for complex workflow orchestration and dependency management
- Databricks for unified analytics platform with collaborative notebooks
- AWS Glue, Azure Synapse Analytics, Google Dataflow for cloud ETL
- Custom Python/Scala data processing with pandas, Polars, Ray
- Data validation and quality monitoring with Great Expectations
- Data profiling and discovery with Apache Atlas, DataHub, Amundsen
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.
- 8d ago First seen · 225 lines · 54 tokens per session scan A c5cb2817417b
data-engineer is a skill published in the GitHub repository rmyndharis/antigravity-skills (1,529 stars, last pushed 1mo ago), licensed MIT. It adds 54 tokens to every session and 2,203 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to data-engineer, differing in 10 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
Professional Ai Engineer skill. Integrate LLM API workflows, safe system prompt guidelines, and agentic workflows.
data-pipeline
Professional Data Pipeline Expert skill. Build robust, automated deployment pipelines and configure cloud infrastructure as code.
embedding-architect
Professional Embedding Architect skill. Integrate LLM API workflows, safe system prompt guidelines, and agentic workflows.