Agent skill · data analytics · aiskillstore
data-engineering-data-driven-feature
Build features guided by data insights, A/B testing, and continuous measurement using specialized agents for analysis, implementation, and experimentation.
Why this skill is useful
Adds detailed workflows for data-driven feature development, including statistical experiment design and analytics instrumentation that aren't commonly found in public documentation.
What it needs
About 6k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
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 subagenttype="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. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/data-engineering-data-driven-feature