What is the AI-powered analytics workflow in Cursor?

The AI-powered analytics workflow is a 7-step system for running data analysis in Cursor IDE using three pillars: Cursor Rules (persistent context with query patterns and DWH documentation), Claude Skills (reusable AI prompts for EDA, data quality, and report generation), and MCP servers (direct authenticated access to Supabase and GitHub). The workflow covers setup, planning, data quality, EDA, analysis, synthesis, and delivering three report formats — cutting analysis time by up to 10x.

What are Cursor Rules for data analytics?

Cursor Rules are persistent, context-aware instructions loaded into every Cursor AI session. For analytics, they encode your data warehouse structure, Supabase query optimization patterns, and business logic so the AI writes consistent, production-ready SQL without needing re-explanation each session. Rules are stored as .mdc files in your project and automatically applied based on file type or task context.

What are Claude Skills in the analytics workflow?

Claude Skills are reusable, parameterized AI prompt templates built for specific analytics tasks. In this workflow, dedicated Skills handle data quality checks, exploratory data analysis (EDA), and report generation — each pre-loaded with your schema.yml and metrics.yml for context-aware, consistent output. Skills eliminate the need to write long prompts from scratch for recurring analysis tasks.

How do MCP servers improve data analysis in Cursor?

MCP (Model Context Protocol) servers give the AI direct, authenticated access to your data sources — such as Supabase, GitHub, or internal documentation — without copying data into the prompt manually. This enables the AI to query live data, read current schema definitions, and commit analysis artifacts (reports, dashboards) directly to GitHub, making the entire analytics workflow end-to-end automated.

What is a semantic model (schema.yml) in analytics?

A semantic model in this workflow consists of two YAML files: schema.yml (describes your tables, columns, relationships, data types, and business context) and metrics.yml (defines business metrics like MRR, churn rate, and DAU with their exact SQL formulas). Loading these into Cursor Rules and Claude Skills ensures every analysis uses consistent, correct metric definitions — eliminating discrepancies between analysts.

How long does the AI analytics workflow take?

A complete end-to-end analytics project using this workflow takes approximately 2.5 hours: 20 minutes for setup (MCP servers, Rules, Skills, semantic model), 15 minutes for planning, 20 minutes for data quality, 25 minutes for EDA, 30 minutes for analysis and query optimization, 20 minutes for synthesis, and 20 minutes for delivering three report formats — executive PDF summary, static HTML report, and interactive dashboard.

The Ultimate AI-Powered Analytics Workflow Playbook

🎓 Analytics Workflow Playbook

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