Data Engineering Data Driven Feature

This orchestrates a complete data-driven feature workflow from exploratory analysis through A/B testing and gradual rollout. You get specialized agents at each phase: data scientists run EDA and design experiments with proper power analysis, engineers build instrumentation with Segment or Amplitude, backend devs implement feature flags with LaunchDarkly, and deployment engineers manage traffic allocation. The workflow is thorough, maybe exhaustively so. If you're building features informed by analytics and want structured handoffs between analysis, implementation, and measurement, this gives you the full playbook. If you just need to ship a feature with basic tracking, it's probably overkill.

skills.sh installs: 317
GitHub stars: 39.4K
last updated: Jun 3, 2026

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Install

$ npx -y skills add sickn33/antigravity-awesome-skills --skill data-engineering-data-driven-feature --agent claude-code

Installs into .claude/skills of the current project.

Files

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.]