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Found 47 Skills
Amazon Redshift is NOT PostgreSQL — corrects PostgreSQL-derived LLM mistakes; covers Redshift-specific SQL, DDL, COPY/UNLOAD, system views, metadata discovery, and operational patterns. Applies ONLY when the task is about Redshift itself (cluster, Serverless workgroup, or Redshift SQL). Pushes back on: CREATE INDEX, string_agg, pg_catalog, text type, SERIAL, stl_query, LATERAL, RETURNING. Triggers on: Redshift SQL, Redshift CREATE TABLE, Redshift COPY/UNLOAD, slow Redshift query, Redshift permission denied, Redshift disk full, Redshift system views, QUALIFY, PIVOT, MERGE, Redshift Data API, Redshift WLM, concurrency scaling, Redshift resize, Redshift Spectrum external tables. Does NOT apply to (defer to that service's own skill): Amazon S3 storage/bucket policies, Athena or Glue queries/catalogs, data-lake or Iceberg work outside Redshift, Aurora, RDS, or DynamoDB — but S3/Glue ARE in scope for Redshift COPY, UNLOAD, or data-lake queries (external schemas/tables on S3).
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.
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
Discover what's in the connected warehouse — schemas, tables, columns, and CARTO named sources.
Discover and subscribe to external spatial datasets via CARTO Data Observatory and partner catalogs.
Context layer for AI data agents - query warehouses accurately with semantic layers, metrics, and wiki knowledge through MCP
Translates Snowflake dbt SQL models to Standardized BigQuery SQL. Handles SQL compilation, Jinja macro placeholder masking, BigQuery Translation Service migration workflows, AST-based config transformations, explicit type casting, JSON extraction standardization, and deduplication. Use when migrating Snowflake dbt pipelines or models to Google Cloud BigQuery. Don't use for generic BigQuery queries or non-Snowflake SQL migrations.
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.
Write spatial SQL against the connected warehouse — dialect-specific guidance, performance defaults, and CARTO's query/job execution model.
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.
Diagnose why a data warehouse sync is failing and recommend the right recovery action. Use when the user asks "why isn't my Stripe/Postgres/Hubspot sync working?", "this table has been stuck for hours", "the data in the warehouse looks wrong", or wants to troubleshoot a specific source or schema. Covers source-level vs schema-level failures, stuck Running states, credential and schema-drift errors, incremental-field misconfig, CDC prerequisite failures, and the cancel / reload / resync / delete-data recovery actions.