Agent skill · data analytics · anthropics
data-context-extractor
Generate or improve a company-specific data analysis skill by extracting tribal knowledge from analysts. BOOTSTRAP MODE - Triggers: "Create a data context skill", "Set up data analysis for our warehouse", "Help me create a skill for our database", "Generate a data skill for [company]" → Discovers schemas, asks key questions, generates initial skill with reference files ITERATION MODE - Triggers: "Add context about [domain]", "The skill needs more info about [topic]", "Update the data skill with [metrics/tables/terminology]", "Improve the [domain] reference" → Loads existing skill, asks targeted questions, appends/updates reference files Use when data analysts want Claude to understand their company's specific data warehouse, terminology, metrics definitions, and common query patterns.
Why this skill is useful
Generates a tailored data analysis skill with specific company context, including schemas, metrics, and common query patterns that the AI cannot infer from general knowledge.
What it needs
Requires bigquery, databricks, postgresql, snowflake installed locally. About 4k tokens when loaded. Last updated 2026-08-07. 23,345 stars on the source repository.
What this skill does
Data Context Extractor A meta-skill that extracts company-specific data knowledge from analysts and generates tailored data analysis skills. How It Works This skill has two modes: 1. Bootstrap Mode: Create a new data analysis skill from scratch 2. Iteration Mode: Improve an existing skill by adding domain-specific reference files --- Bootstrap Mode Use when: User wants to create a new data context skill for their warehouse. Phase 1: Database Connection & Discovery Step 1: Identify the database type Ask: "What data warehouse are you using?" Common options: BigQuery Snowflake PostgreSQL/Redshift Databricks Use ~~data warehouse tools (query and schema) to connect. If unclear, check available MCP tools in the current session. Step 2: Explore the schema Use ~~data warehouse schema tools to: 1. List available datasets/schemas 2. Identify the most important tables (ask user: "Which 3-5 tables do analysts query most often?") 3. Pull schema details for those key tables Sample exploration queries by dialect: Phase 2: Core Questions (Ask These) After schema discovery, ask these questions conversationally (not all at once): Entity Disambiguation (Critical) "When people here say 'user' or 'customer', what exactly do they mean? Are there different types?" Listen for: Multiple entity types (user vs account vs organization) Relationships between them (1:1, 1:many, many:many) Which ID fields link them together Primary Identifiers "What's the main identifier for a [customer/user/account]? Are there multiple IDs for the same entity?" Listen for: Primary keys vs business keys UUID vs integer IDs Legacy ID systems Key Metrics "What are the 2-3 metrics people ask about most? How is each one calculated?" Listen for: Exact formulas (ARR = monthlyrevenue × 12) Which tables/columns feed each metric Time period conventions (trailing 7 days, calendar month, etc.) Data Hygiene "What should ALWAYS be filtered out of queries? (test data, fraud, internal users, etc.)" Listen for: Standard WHERE cl …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills anthropics/data-context-extractor