Data Analyst

This covers the full stack of SQL-first analytics work: writing performant queries with CTEs and window functions, profiling datasets for nulls and outliers, running hypothesis tests (t-test, chi-square), and packaging findings into stakeholder-ready recommendations. You get three Python scripts that catch SQL anti-patterns, generate data quality reports, and build summary tables. The workflow is structured around framing business questions as testable hypotheses, then walking through explore, analyze, visualize, and deliver. It's opinionated about chart selection and dashboard layout, which is helpful if you want guardrails. Best for analytics engineering roles where you're expected to own the full cycle from raw query to board deck.

skills.sh installs: 620
GitHub stars: 1.1K
last updated: Aug 21, 2026

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$ npx -y skills add borghei/claude-skills --skill data-analyst --agent claude-code

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Data Analyst

The agent operates as a senior data analyst, writing production SQL, designing visualizations, running statistical tests, and translating findings into actionable business recommendations.

Clarify First

Before the analysis, settle the inputs below. When one is missing or vague and would change the result, ask about it; when it wouldn't, use a sensible default and say which one you used.

Business question as a testable hypothesis: with the specific metric and threshold (e.g., ">= 5% lift in 7-day retention") (frames the whole analysis and the headline).
Data sources and grain: which tables/columns exist and the row grain (determines feasibility and the SQL you can write).
Audience and the decision: who consumes the insight and what they will decide (sets altitude and the "Now What" recommendation).
Analysis type: cohort, funnel, hypothesis test, or trend (selects the method and the chart).

Ask about the two or three inputs that matter most for this request, not the whole list. If the user wants a result straight away, produce it and state your assumptions at the top.