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-driven-featuregit 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-driven-feature)<a href="https://agentmods.dev/skills/rmyndharis/antigravity-skills/data-engineering-data-driven-feature"><img src="https://agentmods.dev/badge/skills/rmyndharis/antigravity-skills/data-engineering-data-driven-feature/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-driven-feature"><img src="https://agentmods.dev/badge/skills/rmyndharis/antigravity-skills/data-engineering-data-driven-feature.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 | $0.02338 |
| Opus 5 | $0.00016 | $0.01169 |
| Sonnet 5 | $0.00006 | $0.00468 |
| Haiku 4.5 | $0.00003 | $0.00234 |
Grade A, and why
data-engineering-data-driven-feature 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 6d 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
2 near-identical copies found in the catalogue:
- data-engineering-data-driven-feature — 97% identical, 1 lines differ
- data-engineering-data-driven-feature — 95% identical, 9 lines differ
How it starts
The opening of the file, as written. The whole thing — 182 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data-Driven Feature Development
Build features guided by data insights, A/B testing, and continuous measurement using specialized agents for analysis, implementation, and experimentation.
[Extended thinking: This workflow orchestrates a comprehensive data-driven development process from initial data analysis and hypothesis formulation through feature implementation with integrated analytics, A/B testing infrastructure, and post-launch analysis. Each phase leverages specialized agents to ensure features are built based on data insights, properly instrumented for measurement, and validated through controlled experiments. The workflow emphasizes modern product analytics practices, statistical rigor in testing, and continuous learning from user behavior.]
Use this skill when
- Working on data-driven feature development tasks or workflows
- Needing guidance, best practices, or checklists for data-driven feature development
Do not use this skill when
- The task is unrelated to data-driven feature development
- 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.
Phase 1: Data Analysis and Hypothesis Formation
1. Exploratory Data Analysis
- Use Task tool with subagent_type="machine-learning-ops::data-scientist"
- Prompt: "Perform exploratory data analysis for feature: $ARGUMENTS. Analyze existing user behavior data, identify patterns and opportunities, segment users by behavior, and calculate baseline metrics. Use modern analytics tools (Amplitude, Mixpanel, Segment) to understand current user journeys, conversion funnels, and engagement patterns."
- Output: EDA report with visualizations, user segments, behavioral patterns, baseline metrics
2. Business Hypothesis Development
- Use Task tool with subagent_type="business-analytics::business-analyst"
- Context: Data scientist's EDA findings and behavioral patterns
- Prompt: "Formulate business hypotheses for feature: $ARGUMENTS based on data analysis. Define clear success metrics, expected impact on key business KPIs, target user segments, and minimum detectable effects. Create measurable hypotheses using frameworks like ICE scoring or RICE prioritization."
- Output: Hypothesis document, success metrics definition, expected ROI calculations
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.
- 6d ago First seen · 182 lines · 32 tokens per session scan A 4e8b7d17afd8
data-engineering-data-driven-feature is a skill published in the GitHub repository rmyndharis/antigravity-skills (1,517 stars, last pushed 1mo ago), licensed MIT. It adds 32 tokens to every session and 2,338 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.
Other skills, from other repositories
code-review
Systematic code review skill covering both requesting a review (pre-commit checklist) and receiving and responding to review feedback. Checks code quality, security, test coverage, architectural alignment, and documentation before any code is committed.
writing-plans
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brainstorming
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project-context-primer
Run this skill at the very start of any new conversation or agent session before writing a single line of code. It loads the project's architectural decisions, conventions, known gotchas, and current task status so the agent operates with full context — not as a blank slate.
test-driven-execution
Before writing any implementation code, define the acceptance criteria and test cases that the code must satisfy. Agents then write code to pass these tests — not to match a vague description. Eliminates "it works on my machine" and "I think this is what you wanted" outcomes.
idea-validator
Structured validation framework that scores product ideas. Use when evaluating problem severity, willingness-to-pay, or founder-market fit. For market intelligence, see market-research.