Data engineering toolkit

Data-engineering skills for choosing where data should live, laying out distribution keys and partitions in MPP warehouses, and designing pipelines with data quality, reconciliation and governance built in.

Part of Business Analytics Skills, a set of agent skills that make an AI assistant work like a decision scientist and an advisor. Each skill asks what the work is for only when the answer would change the result, follows a real method step by step, runs a check wherever there is logic to verify, and hands back a plain-language brief plus a technical appendix.

Skills (3)

• datastore-selection: Decide where data should live: a relational OLTP database, a columnar warehouse, a document store, a key-value cache, a graph database, a data lake or lakehouse, or a combination.
• distribution-key-and-partitioning: Choose how a large table is spread across nodes and split into partitions in an MPP warehouse or distributed store, and fix the skew and data movement that make queries slow.
• pipeline-and-quality-design: Design how data moves from source systems to its consumers, and how its quality is proven along the way.

Use

Skills load by themselves when a request matches. Ask a normal work question; to call one directly, type / and the skill name in Claude Code, Cursor or Copilot, or $ and the name in Codex.

What it runs

Instructions only, plus small optional Python checks (scripts/, standard library, numpy, pandas or scikit-learn) that read only the file you point them at. Nothing here reads credentials, environment secrets or your files on its own, makes network calls, or sends data anywhere.

Licence

MIT. Source and issues: https://github.com/moheetsubudhi-isb/business-analytics-skills