data-engineering-data-driven-feature

version	4.1.0-fractal
name	data-engineering-data-driven-feature
description	Build features guided by data insights, A/B testing, and continuous measurement using specialized agents for analysis, implementation, and experimentation.

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.
• If detailed examples are required, open resources/implementation-playbook.md.

Phase 1: Data Analysis and Hypothesis Formation

🧠 Knowledge Modules (Fractal Skills)

1. 1. Exploratory Data Analysis

2. 2. Business Hypothesis Development

3. 3. Statistical Experiment Design

4. 4. Feature Architecture Planning

5. 5. Analytics Instrumentation Design

6. 6. Data Pipeline Architecture

7. 7. Backend Implementation

8. 8. Frontend Implementation

9. 9. ML Model Integration (if applicable)

10. 10. Analytics Validation

11. 11. Experiment Setup

12. 12. Gradual Rollout

13. 13. Real-time Monitoring

14. 14. Statistical Analysis

15. 15. Business Impact Assessment

16. 16. Post-Launch Optimization