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Found 41 Skills
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.
Change the sync configuration of an existing data warehouse schema — switch sync_type, pick a different incremental_field, set primary_key_columns, choose cdc_table_mode, or change sync_frequency. Use when the user asks "switch my orders table from full refresh to incremental", "this table is syncing too slowly / too frequently", "I need to pick a different incremental column", "set up CDC for this Postgres table", or when diagnosis of a failing sync pointed to an incremental-field or PK misconfiguration.
Design data pipelines covering ETL vs ELT architectures, data source integration, scheduling, quality checks, and warehouse design. Use this skill when the user needs to move data between systems, build a data warehouse, automate data processing, or improve data reliability — even if they say 'move data from X to Y', 'build an ETL pipeline', 'our data is a mess', or 'set up a data warehouse'.
Plan a migration onto MotherDuck. Use when moving from Snowflake, Redshift, PostgreSQL, dbt-heavy stacks, or lakehouse tooling and the key decisions are target pattern, cutover slices, validation, rollback, and native-versus-DuckLake posture.
Discover and subscribe to external spatial datasets via CARTO Data Observatory and partner catalogs.
Write spatial SQL against the connected warehouse — dialect-specific guidance, performance defaults, and CARTO's query/job execution model.
Builds, schedules, and operates analytics DAGs in CARTO Workflows — the no-code/low-code orchestration layer over the data warehouse. Triggers when the user wants to author a workflow, run/edit one, schedule a DAG, or copy a workflow across profiles or orgs.
Import geospatial files into the data warehouse via CARTO, export results back out, and prepare tilesets for fast map rendering.
Choose and configure the data warehouse engine connection for CARTO (BigQuery, Snowflake, Redshift, Postgres, Databricks, Oracle).
Install and configure ktx, the self-improving context layer that teaches AI agents to query data warehouses accurately with approved metrics, semantic layer, and business knowledge.