Data Engineering Data Driven Feature Agent Skill
Build features guided by data insights, A/B testing, and continuous measurement using specialized agents for analysis, implementation, and experimentation.
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/Dokhacgiakhoa/Agent-Skills-4-Vibe-Coding-CLI --skill data-engineering-data-driven-featureWhat comes with it
10 386 bytes besides the instructionThe instruction itself
22 sections, as written by the authorData-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
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How to use it
Take dokhacgiakhoa/data-engineering-data-driven-feature from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.