data-sourcing

name	data-sourcing
description	Data collection and readiness — the step BEFORE a pipeline exists. Catalog candidate data sources for an objective, rank each by importance/impact, and record where to get it (internal system vs. open/public dataset), how to access it, its refresh cadence, licensing, and readiness blockers. Use this skill whenever a project is starting and the sources aren't decided yet, when the user asks "what data do I need for X and where do I get it", when weighing internal vs. open-source data, or when assessing whether a source is accessible/usable before committing to a build. This sits upstream of pipeline-design — it decides WHAT to ingest before deciding HOW.
origin	grouped

Data Sourcing & Readiness

The step before you build anything. You cannot design a pipeline for data you haven't located, priced, or confirmed you're allowed to use. This skill produces a source inventory the rest of the lifecycle consumes.

Start from the objective, not the data

Ask first: what question or product does this data serve? Every source is judged by how much it moves that objective — not by how easy it is to grab.

1. Objective — BI dashboard? ML model? Regulatory report? One-off analysis?
2. Decision the data drives — what changes based on this data?
3. Minimum viable sources — the smallest set that answers the objective. Resist collecting "nice to have" data no one will use.

Build the source inventory

For every candidate source, fill one row. This table is the deliverable.

Column	What to capture