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Found 10 Skills
Use this skill when building real-time or near-real-time data pipelines. Covers Kafka, Flink, Spark Streaming, Snowpipe, BigQuery streaming, materialized views, and batch-vs-streaming decisions. Common phrases: "real-time pipeline", "Kafka consumer", "streaming vs batch", "low latency ingestion". Do NOT use for batch integration patterns (use integration-patterns-skill) or pipeline orchestration (use data-orchestration-skill).
Configures and integrates SAP Master Data Integration (MDI) service on SAP Business Technology Platform. Use when setting up MDI tenants, connecting applications (S/4HANA, SuccessFactors, Ariba, Fieldglass, etc.), configuring distribution models, SOAP APIs for business partners, extensibility, or troubleshooting master data replication. Covers One Domain Model integration, Business Data Orchestration, client authentication (OAuth2, mTLS), and security configurations.
Build complete Salesforce Lightning Experience applications from natural language descriptions. Use this skill when a user requests a "complete app", "Lightning app", "business solution", "management system", or describes a scenario requiring multiple interconnected Salesforce components (objects, fields, pages, tabs, security). Orchestrates all required metadata types in proper dependency order to produce a deployable application.
Skill that helps users discover and understand Dagster integration libraries. Used when users have requests related to integrating with other tools / technologies, or when have users have questions related to specific integration libraries (dagster-*).
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'.
Primary entry point for building, managing, and orchestrating data pipelines on Google Cloud. Guides users to the appropriate skill for dbt, Dataflow (Apache Beam), Dataform, Spark (Dataproc Serverless), BigQuery Data Transfer Service (DTS) or orchestration pipeline using Cloud Composer. Clarify requirements and resolve ambiguity for creating, updating and running data pipelines.
Astronomer integration. Manage data, records, and automate workflows. Use when the user wants to interact with Astronomer data.
Optimize BigQuery compute costs by assigning data models (Dataform, dbt, Airflow) to slot reservations or on-demand compute based on Masthead recommendations.
Observability and monitoring for data pipelines using OpenTelemetry (traces) and Prometheus (metrics). Covers instrumentation, dashboards, and alerting.
Production ETL patterns orchestrator. Routes to core reliability patterns and incremental load strategies.