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-engineering-data-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/data-engineering-data-pipeline)<a href="https://agentmods.dev/skills/rmyndharis/antigravity-skills/data-engineering-data-pipeline"><img src="https://agentmods.dev/badge/skills/rmyndharis/antigravity-skills/data-engineering-data-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/data-engineering-data-pipeline"><img src="https://agentmods.dev/badge/skills/rmyndharis/antigravity-skills/data-engineering-data-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 | $0.00032 | $0.01495 |
| Opus 5 | $0.00016 | $0.00747 |
| Sonnet 5 | $0.00006 | $0.00299 |
| Haiku 4.5 | $0.00003 | $0.00150 |
Grade A, and why
data-engineering-data-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 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:
- data-engineering-data-pipeline — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 202 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Pipeline Architecture
You are a data pipeline architecture expert specializing in scalable, reliable, and cost-effective data pipelines for batch and streaming data processing.
Use this skill when
- Working on data pipeline architecture tasks or workflows
- Needing guidance, best practices, or checklists for data pipeline architecture
Do not use this skill when
- The task is unrelated to data pipeline architecture
- You need a different domain or tool outside this scope
Requirements
$ARGUMENTS
Core Capabilities
- Design ETL/ELT, Lambda, Kappa, and Lakehouse architectures
- Implement batch and streaming data ingestion
- Build workflow orchestration with Airflow/Prefect
- Transform data using dbt and Spark
- Manage Delta Lake/Iceberg storage with ACID transactions
- Implement data quality frameworks (Great Expectations, dbt tests)
- Monitor pipelines with CloudWatch/Prometheus/Grafana
- Optimize costs through partitioning, lifecycle policies, and compute optimization
Instructions
1. Architecture Design
- Assess: sources, volume, latency requirements, targets
- Select pattern: ETL (transform before load), ELT (load then transform), Lambda (batch + speed layers), Kappa (stream-only), Lakehouse (unified)
- Design flow: sources → ingestion → processing → storage → serving
- Add observability touchpoints
2. Ingestion Implementation
Batch
- Incremental loading with watermark columns
- Retry logic with exponential backoff
- Schema validation and dead letter queue for invalid records
- Metadata tracking (_extracted_at, _source)
Streaming
- Kafka consumers with exactly-once semantics
- Manual offset commits within transactions
- Windowing for time-based aggregations
- Error handling and replay capability
3. Orchestration
Airflow
- Task groups for logical organization
- XCom for inter-task communication
- SLA monitoring and email alerts
- Incremental execution with execution_date
- Retry with exponential backoff
Prefect
- Task caching for idempotency
- Parallel execution with .submit()
- Artifacts for visibility
- Automatic retries with configurable delays
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 · 202 lines · 32 tokens per session scan A 2892e22f8222
data-engineering-data-pipeline is a skill published in the GitHub repository rmyndharis/antigravity-skills (1,502 stars, last pushed 1mo ago), licensed MIT. It adds 32 tokens to every session and 1,495 once invoked, about $0.0002 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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prompt-enhancer
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