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Found 95 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).
Extract and process energy data from BSEE (Gulf of Mexico) and SODIR (Norway) regulatory databases
Data Catalog Updater - Auto-activating skill for Data Pipelines. Triggers on: data catalog updater, data catalog updater Part of the Data Pipelines skill category.
Airflow Operator Creator - Auto-activating skill for Data Pipelines. Triggers on: airflow operator creator, airflow operator creator Part of the Data Pipelines skill category.
Master Node.js streams for memory-efficient processing of large datasets, real-time data handling, and building data pipelines
Consult this skill when designing data pipelines or transformation workflows. Use when data flows through fixed sequence of transformations, stages can be independently developed and tested, parallel processing of stages is beneficial. Do not use when selecting from multiple paradigms - use architecture-paradigms first. DO NOT use when: data flow is not sequential or predictable. DO NOT use when: complex branching/merging logic dominates.
Production ETL patterns orchestrator. Routes to core reliability patterns and incremental load strategies.
Python DAG workflow orchestration using Apache Airflow for data pipelines, ETL processes, and scheduled task automation
Excel to CSV conversion skill. Convert specific bounding tables or entire worksheets within `.xlsx` or `.xls` binary formats into flat `.csv` tabular data. Use this when you find an Excel file and need its data mapped into an accessible format for text analysis, filtering, or programmatic pipelining.
Import/export pipeline for UnoPim. Activates when configuring imports, exports, debugging job pipelines, or creating data transfer profiles; or when the user mentions import, export, CSV, Excel, job, queue, batch, or data transfer.
Run a comprehensive data quality assessment and produce a scorecard across 6 dimensions: completeness, uniqueness, consistency, timeliness, accuracy, validity. Use when the user asks about data quality, mentions data issues, wants to audit a table, is onboarding a new data source, or needs to validate pipeline output.