ds

Orchestrate a multi-phase data science workflow from objective clarification to user acceptance.

19|5|Updated Jan 4, 2026
One-click install
npx skills add https://github.com/edwinhu/workflows --skill ds
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: ds
Source: https://github.com/edwinhu/workflows/tree/main/skills/ds
Command: npx skills add https://github.com/edwinhu/workflows --skill ds

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) and scripts (resource) components.

What problem does it solve?

This Skill guides users through the complex process of defining, planning, and executing data science projects, ensuring clarity of objectives and a structured approach to analysis.

Core Features & Use Cases

  • Objective Clarification: Uses Socratic questioning to refine vague analysis requests into clear, actionable objectives.
  • Data Source Identification: Helps identify and document necessary data sources and their constraints.
  • Approach Selection: Facilitates the selection of appropriate analysis methodologies.
  • Structured Planning: Generates a SPEC.md document outlining the project's goals, data, success criteria, and chosen approach.
  • Use Case: A marketing team wants to understand customer churn. This skill will help them define precisely what "churn" means, what data is needed (e.g., customer demographics, purchase history, support interactions), and what constitutes a successful analysis before any data is touched.

Quick Start

Use the ds skill to start planning a new data analysis project by clarifying objectives and data requirements.

Frequently Asked Questions about ds

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I plan a data science project from scratch?

To plan a data science project, this skill orchestrates a multi-phase workflow starting with objective clarification through Socratic questioning. It systematically refines analysis requests, identifies data sources and constraints, and ensures user confirmation before proceeding to data exploration and implementation.

What is the best way to define data analysis objectives before touching data?

Defining data analysis objectives is best handled through Socratic questioning, which this skill uses to refine vague requests into clear, actionable goals. It documents these goals, necessary data sources, and success criteria in a SPEC.md file before any data exploration begins.

How does Socratic questioning improve data science workflow planning?

Socratic questioning improves data science workflow planning by systematically refining ambiguous analysis requests into precise, actionable objectives. This method ensures that project goals, data source constraints, and appropriate methodologies are thoroughly evaluated and documented before implementation begins.

Can I generate a project specification document for a data science workflow?

Yes, you can generate a project specification document by using this skill to enforce a structured planning process. It produces a SPEC.md file outlining the project's goals, required data sources, success criteria, and chosen analysis approach after user confirmation.

How do I identify data sources and constraints for an analysis project?

To identify data sources and constraints for an analysis project, this skill guides you through a structured planning workflow. It systematically evaluates your clarified objectives to document necessary data inputs and their limitations before selecting an appropriate methodology.