data-engineering-data-driven-featurelisted

# 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 ### 1. Exploratory Data Analysis - Use Task tool with subagent_type="machine-learning-ops::data-scientist"