data-sourcinglisted
Install: claude install-skill Methasit-Pun/data_engineer_claude_skills
# 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 |
|---|---|
| Source name | Human name (e.g. "Salesforce Opportunities", "OpenStreetMap") |
| Type | Internal system / SaaS API / public dataset / file drop / scrape |
| Importance | Critical / important / enrichment — impact on the objective |
| Where to get it | Exact system, endpoint, URL, or dataset repository |
| Access method | DB credential, API key, OAuth, S3 bucket, download, request-to-owner |
| Owner | Team or person who controls access |
| Refresh cadence | Real-time / daily / monthly / static |
| Volume | Rough row/byte scale — feeds architecture sizing |
| Licensing | Internal-only / open license (name it) / restricted / P