Data Science Strategy & Brainstorming FAQs

Question 1

Why can't I load data during the brainstorming phase?

Accepted Answer

The 'Iron Law' of this skill prevents aimless exploration. Defining objectives first ensures that subsequent data profiling is purposeful and avoids 'pattern-matching' errors where the AI assumes goals that don't exist.

Question 2

How does this fit into the larger Data Science workflow?

Accepted Answer

This is Phase 1 (Brainstorm) of a 5-phase workflow. Once objectives are confirmed, it automatically transitions into Phase 2 (Planning), followed by Implementation, Review, and Verification.

Question 3

Can I skip the questioning process?

Accepted Answer

No. To ensure high-quality outcomes, the skill enforces a structured interview. Skipping this phase often leads to wrong objectives and wasted analysis time.

Question 4

Does this skill support replicating existing work?

Accepted Answer

Yes, it includes specific checks to identify if you are replicating or extending previous analysis, ensuring that specific methodologies and results are matched exactly when required.

Question 5

What is the primary output of the ds skill?

Accepted Answer

The primary output is a .claude/SPEC.md file which documents the project's objectives, data sources, success criteria, and chosen approach to serve as a source of truth for the rest of the workflow.

Data Science Strategy & Brainstorming

Características Principales

Casos de Uso

Data Science Strategy & Brainstorming

Características Principales

Casos de Uso