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Found 32 Skills
Provide a lookup index of dbt models (BigQuery tables) to guide query writing against a data warehouse. Use when you need to query, analyze, or look up data in a dbt-powered data warehouse, or when resolving a vague data question into the right BigQuery tables to query.
Import geospatial files into the data warehouse via CARTO, export results back out, and prepare tilesets for fast map rendering.
Use when large data ingestion, backfill, export, ETL, warehouse loading, manifest catch-up, or table synchronization needs to become much faster while preserving data correctness.
Write correct, performant SQL across all major data warehouse dialects (Snowflake, BigQuery, Databricks, PostgreSQL, etc.). Use when writing queries, optimizing slow SQL, translating between dialects, or building complex analytical queries with CTEs, window functions, or aggregations.
ByteHouse Slow Query Analysis and Performance Optimization Tool, used to identify and analyze slow queries, provide query performance optimization suggestions, view query execution plans, and analyze query historical trends. Use this Skill when you need to identify and analyze slow queries in ByteHouse database, get query performance optimization suggestions, view query execution plans, or analyze query historical trends.
Audit the health of a PostHog project's data warehouse — find every broken or degraded pipeline item across sources, sync schemas, materialized views, batch exports, and transformations. Use when the user asks "what's broken in my warehouse?", "give me a health check", "audit my data pipeline", "why are some dashboards stale?", or wants a one-shot triage summary before deciding where to spend time. Produces a prioritized report of issues grouped by severity and type, with recommended next steps.
Discover what's in the connected warehouse — schemas, tables, columns, and CARTO named sources.
Context layer for AI data agents - teach Claude Code, Codex, and AI agents to query data warehouses accurately with semantic layer, wiki knowledge, and MCP tools
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.
Snowflake integration. Manage data, records, and automate workflows. Use when the user wants to interact with Snowflake data.
Build and maintain an executable context layer for data and analytics agents using ktx's semantic layer, wiki knowledge, and MCP integration
Write spatial SQL against the connected warehouse — dialect-specific guidance, performance defaults, and CARTO's query/job execution model.