Agent skill · data analytics · borghei
data-analyst
Data analysis across SQL, visualization, statistics, and reporting. Use when writing SQL queries, building dashboards, performing cohort or funnel analysis, running hypothesis tests, or presenting data-driven recommendations.
Why this skill is useful
Provides specific SQL patterns and statistical methods that enhance the AI's ability to perform complex data analyses and visualizations.
What it needs
About 7k tokens when loaded. Last updated 2026-07-21. 462 stars on the source repository.
What this skill does
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, confirm these inputs. If any is unknown or vague, ASK — do not assume: [ ] 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) Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact. Workflow 1. Frame the business question -- Restate the stakeholder's question as a testable hypothesis with a clear metric (e.g., "Did campaign X increase 7-day retention by >= 5%?"). Identify required data sources. 2. Write and validate SQL -- Use CTEs for readability. Filter early, aggregate late. Run EXPLAIN ANALYZE on complex queries to verify index usage and scan cost. 3. Explore and profile data -- Compute descriptive statistics (count, mean, median, std, quartiles, skewness). Check for nulls, duplicates, and outliers before drawing conclusions. 4. Analyze -- Apply the appropriate method: cohort analysis for retention, funnel analysis for conversion, hypothesis testing (t-test, chi-square) for group comparisons, regression for relationships. 5. Visualize -- Select chart type from the matrix below. Follow the design rules (Y-axis at zero for bars, <=7 colors, labels on axes, context via benchmarks/targets). 6. Deliver the insight -- Structure findings as What / So What / Now What. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills borghei/data-analyst