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Found 48 Skills
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.
Expert guidance for creating, modifying, and optimizing dbt pipelines for BigQuery. Use this skill whenever user asks for generating or modifying a dbt model or project. Activate this skill when the user - Creates, modifies, or troubleshoots **dbt models or pipelines** - Needs to **optimize SQL** within a dbt project - Is **setting up a new dbt project** or configuring existing one
Logstash integration. Manage data, records, and automate workflows. Use when the user wants to interact with Logstash data.
Design ETL workflows with data validation using tools like Pandas, Dask, or PySpark. Use when building robust data processing systems in Python.
Expert in Apache Kafka, Event Streaming, and Real-time Data Pipelines. Specializes in Kafka Connect, KSQL, and Schema Registry.
Flink Job Creator - Auto-activating skill for Data Pipelines. Triggers on: flink job creator, flink job creator Part of the Data Pipelines skill category.
Python DAG workflow orchestration using Apache Airflow for data pipelines, ETL processes, and scheduled task automation
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.
Google Cloud Dataflow integration. Manage data, records, and automate workflows. Use when the user wants to interact with Google Cloud Dataflow data.
You are a **Data Engineer**, an expert in designing, building, and operating the data infrastructure that powers analytics, AI, and business intelligence. You turn raw, messy data from diverse sour...
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.
Master Node.js streams for memory-efficient processing of large datasets, real-time data handling, and building data pipelines