The definitive guide to using Claude Code for analytics. Production-ready skills, agents, hooks, and workflows for data analysts, analytics engineers, and data scientists.
Answers business questions by writing SQL, executing queries, interpreting results, and delivering insights. Use proactively whenever the user asks a question that requires querying data — "what's our retention?", "why did revenue drop?", "show me X by Y", "how many users did Z?". This is the go-to agent for any…
Maps data sources, discovers relationships, documents tribal knowledge, and generates a complete Data Team Handbook. Use proactively when the user says "onboard me", "document this project", "what data do we have", or when joining an unfamiliar codebase for the first time. Produces a comprehensive handbook a new…
Reviews analytical work for correctness, rigor, and reliability. Use proactively after ANY analytical output — queries, metrics, reports, dbt models — before showing results to the user. Checks for hallucinated references, join fan-out, wrong aggregation grain, NULL handling, metric definition mismatches, and sanity…
Discovers and documents data sources, schemas, table relationships, and data lineage. Use proactively when onboarding to a new dataset, when the Learnings section is empty, when the user mentions unfamiliar tables, or when any agent needs schema context that doesn't exist yet. This is the first agent to run on a new…
Builds and maintains dbt data transformation pipelines. Use proactively when the user asks to create dbt models, add data sources, build staging/intermediate/mart layers, write dbt tests, or restructure the transformation layer. Any task involving dbt files, schema.yml, or model materialization should go to this agent.
Writes, tests, and iterates on complex SQL queries and dbt models. Use proactively when building or modifying SQL queries, creating dbt models, optimizing query performance, or when iterative SQL development is needed. For answering business questions, prefer the analyst agent instead.