Umbrella skill for running a data project end-to-end through its lifecycle stages — discover sources → profile the data → architect the platform → build the medallion pipeline → refactor the code. Use this whenever the user is kicking off a new data project,…
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,…
Profile and map raw data BEFORE designing a schema. One-time exploratory analysis to learn the true shape of a dataset — row/column counts, null rates, cardinality, value distributions, ranges, data types, candidate keys, duplicates, referential relationships…
Umbrella skill for shaping data once it has landed — warehouse schema design (star/snowflake/OBT/SCD/grain), SQL for analytics (window functions, CTEs, optimization), dbt model layers/tests/macros/incrementals, and Python transforms (pandas/Polars/PySpark…
dbt model design, ref chains, sources, tests, macros, incremental strategies, materializations, and documentation best practices. Use this skill whenever the user is writing or reviewing dbt models, configuring dbt tests, designing model layers…
Pandas, Polars, and PySpark idioms for production data engineering — chunked reads, memory-safe transforms, vectorized operations, type optimization, and performance patterns. Use this skill whenever the user is writing a Python data transformation script and…
Data modeling for analytical workloads — star schema, snowflake schema, one big table (OBT), slowly changing dimensions (SCD), normalization tradeoffs, grain definition, and surrogate key strategies. Use this skill whenever the user is designing or reviewing…
Best-practice SQL for analytical workloads — window functions, CTEs, query optimization, partitioning strategies, and anti-patterns to avoid. Use this skill whenever the user is writing or reviewing a SQL query that goes beyond a basic SELECT, especially on…