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Found 8 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).
Salesforce Data Cloud Prepare phase. TRIGGER when: user creates or manages Data Cloud data streams, DLOs, transforms, or Document AI configurations, or asks about ingestion into Data Cloud. DO NOT TRIGGER when: the task is connection setup only (use sf-datacloud-connect), DMOs and identity resolution (use sf-datacloud-harmonize), or query/search work (use sf-datacloud-retrieve).
Query Catalog, database, and table metadata resources in Alibaba Cloud Data Lake Formation (DLF). Provides read-only queries via the DLF OpenAPI Python SDK, supporting listing and viewing Catalogs, databases, tables with their detailed information and Schema definitions. Use cases: "list available Catalogs", "list databases", "view table schema", "search tables", "search tables by name", "fuzzy search", "view DLF metadata", "what databases are in the data lake", "what columns does a table have", "find tables whose name contains xxx". This Skill only contains read-only operations — no create, modify, or delete operations.
Strategic guidance for designing modern data platforms, covering storage paradigms (data lake, warehouse, lakehouse), modeling approaches (dimensional, normalized, data vault, wide tables), data mesh principles, and medallion architecture patterns. Use when architecting data platforms, choosing between centralized vs decentralized patterns, selecting table formats (Iceberg, Delta Lake), or designing data governance frameworks.
Bronze/Silver/Gold layer design patterns and templates for building scalable data lakehouse architectures. Includes incremental processing, data quality checks, and optimization strategies.
Use when managing Alibaba Cloud Data Lake Formation (DlfNext) via OpenAPI/SDK, including the user needs DLF Next catalog/governance resource operations, including listing resources, create/update flows, status checks, and troubleshooting metadata workflow issues.
Create managed Iceberg tables using Amazon S3 Tables (s3tables API namespace) with automatic compaction and snapshot management. Sets up table bucket, namespace, table, schema, Glue catalog registration, partitioning, IAM access control. Triggers on: create table, data lake table, analytics table, structured data storage, S3 Tables, Iceberg, Athena table, partitioning strategy, access permissions. Do NOT use for: importing files (use ingesting-into-data-lake), vector storage (use storing-and-querying-vectors), querying existing tables (use querying-data-lake), or locating existing table (use finding-data-lake-assets).
Create and adapt Bolt Pipeliner ETL projects using Spark, Pandas, or Polars. Use whenever a user asks to create an ETL pipeline, data lake pipeline, medallion architecture, Airflow pipeline, data-quality workflow, or Bolt Pipeliner project.