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Found 108 Skills
Apache Airflow workflow orchestration. Use for data pipelines.
Data Quality Checker - Auto-activating skill for Data Pipelines. Triggers on: data quality checker, data quality checker Part of the Data Pipelines skill category.
Build scalable data pipelines, modern data warehouses, and real-time streaming architectures. Implements Apache Spark, dbt, Airflow, and cloud-native data platforms. Use PROACTIVELY for data pipeline design, analytics infrastructure, or modern data stack implementation.
Reporting pipelines for CSV/JSON/Markdown exports with timestamped outputs, summaries, and post-processing.
High-performance Rust web crawler with stealth mode, LLM-ready Markdown export, multi-format output, sitemap discovery, and robots.txt support. Optimized for content extraction, site mapping, structure analysis, and LLM/RAG pipelines.
Schema Validator - Auto-activating skill for Data Pipelines. Triggers on: schema validator, schema validator Part of the Data Pipelines skill category.
Implement data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rules, or establishing data contracts.
Data engineering skill for building scalable data pipelines, ETL/ELT systems, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, and modern data stack. Includes data modeling, pipeline orchestration, data quality, and DataOps. Use when designing data architectures, building data pipelines, optimizing data workflows, implementing data governance, or troubleshooting data issues.
Expert-level Apache Airflow orchestration, DAGs, operators, sensors, XComs, task dependencies, and scheduling
This skill should be used when the user asks to "validate a DataFrame with pandera", "write a pandera schema", "use pandera DataFrameModel", "add data validation to a pipeline", or needs guidance on pandera best practices for data quality.
Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.
Debugs and fixes dbt errors systematically. Use when working with dbt errors for: (1) Task mentions "fix", "error", "broken", "failing", "debug", "wrong", or "not working" (2) Compilation Error, Database Error, or test failures occur (3) Model produces incorrect output or unexpected results (4) Need to troubleshoot why a dbt command failed Reads full error, checks upstream first, runs dbt build (not just compile) to verify fix.