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Found 26 Skills
Complete guide for Apache Airflow orchestration including DAGs, operators, sensors, XComs, task dependencies, dynamic workflows, and production deployment
Expert-level Apache Airflow orchestration, DAGs, operators, sensors, XComs, task dependencies, and scheduling
Quality control metrics and filtering thresholds for protein design. Use this skill when: (1) Evaluating design quality for binding, expression, or structure, (2) Setting filtering thresholds for pLDDT, ipTM, PAE, (3) Checking sequence liabilities (cysteines, deamidation, polybasic clusters), (4) Creating multi-stage filtering pipelines, (5) Computing PyRosetta interface metrics (dG, SC, dSASA), (6) Checking biophysical properties (instability, GRAVY, pI), (7) Ranking designs with composite scoring. This skill provides research-backed thresholds from binder design competitions and published benchmarks.
Flink Job Creator - Auto-activating skill for Data Pipelines. Triggers on: flink job creator, flink job creator Part of the Data Pipelines skill category.
Apache Airflow workflow orchestration. Use for data pipelines.
Develops data processing pipelines, integrations, and machine learning scenarios in SAP Data Intelligence Cloud. Use when building graphs/pipelines with operators, integrating ABAP/S4HANA systems, creating replication flows, developing ML scenarios with JupyterLab, or using Data Transformation Language functions. Covers Gen1/Gen2 operators, subengines (Python, Node.js, C++), structured data operators, and repository objects.
Prefect Flow Builder - Auto-activating skill for Data Pipelines. Triggers on: prefect flow builder, prefect flow builder Part of the Data Pipelines skill category.
Data Quality Checker - Auto-activating skill for Data Pipelines. Triggers on: data quality checker, data quality checker Part of the Data Pipelines skill category.
Expert data analysis and manipulation for customer support operations using pandas
Stream Light Protocol account state via Laserstream gRPC. Covers token accounts, mint accounts, and compressible PDAs with hot/cold lifecycle tracking. Use when building custom data pipelines, aggregators, or indexers.
Interactive tutorial that teaches Snowflake Dynamic Tables hands-on. The agent guides users step-by-step through building data pipelines with automatic refresh, incremental processing, and CDC patterns. Use when the user wants to learn dynamic tables, build a DT pipeline, or understand DT vs streams/tasks/materialized views.
Guides understanding and working with Apache Beam runners (Direct, Dataflow, Flink, Spark, etc.). Use when configuring pipelines for different execution environments or debugging runner-specific issues.