stock-data-skill
OpenClaw Skill for stock data analysis
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.
Suivez les instructions du dépôt pour votre agent. Consultez les fichiers et dépendances avant l’installation.
SkillHub CLI
skillhub install alirezarezvani/senior-data-engineerExaminez la source et la compatibilité, puis suivez les étapes d’installation.
Donnez le lien à votre agent et demandez-lui d’examiner instructions et dépendances avant d’expliquer les étapes.
Production-grade data engineering skill for building scalable, reliable data systems.
Activate this skill when you see:
Pipeline Design:
Architecture:
Data Modeling:
Data Quality:
Performance:
# Generate pipeline orchestration config
python scripts/pipeline_orchestrator.py generate \
--type airflow \
--source postgres \
--destination snowflake \
--schedule "0 5 * * *"
# Validate data quality
python scripts/data_quality_validator.py validate \
--input data/sales.parquet \
--schema schemas/sales.json \
--checks freshness,completeness,uniqueness
# Optimize ETL performance
python scripts/etl_performance_optimizer.py analyze \
--query queries/daily_aggregation.sql \
--e...
Ces informations proviennent de l’analyse indexée et peuvent être anciennes. Vérifiez les fichiers actuels.
# Senior Data Engineer Production-grade data engineering skill for building scalable, reliable data systems. ## Table of Contents 1. [Trigger Phrases](#trigger-phrases) 2. [Quick Start](#quick-start) 3. [Workflows](#workflows) - [Building a Batch ETL Pipeline](#workflow-1-building-a-batch-etl-pipeline) - [Implementing Real-Time Streaming](#workflow-2-implementing-real-time-streaming) - [Data Quality Framework Setup](#workflow-3-data-quality-framework-setup) 4. [Architecture Decision Framework](#architecture-decision-framework) 5. [Tech Stack](#tech-stack) 6. [Reference Documentation](#reference-documentation) 7. [Troubleshooting](#troubleshooting) --- ## Trigger Phrases Activate this skill when you see: **Pipeline Design:** - "Design a data pipeline for..." - "Build an ETL/ELT process..." - "How should I ingest data from..." - "Set up data extraction from..." **Architecture:** - "Should I use batch or streaming?" - "Lambda vs Kappa architecture" - "How to handle late-arriving data" - "Design a data lakehouse" **Data Modeling:** - "Create a dimensional model..." - "Star schema vs snowflake" - "Implement slowly changing dimensions" - "Design a data vault" **Data Quality:** - "Add data validation to..." - "Set up data quality checks" - "Monitor data freshness" - "Implement data contracts" **Performance:** - "Optimize this Spark job" - "Query is running slow" - "Reduce pipeline execution time" - "Tune Airflow DAG" --- ## Quick Start ### Core Tools ```bash # Generate pipeline orchestration config python scripts/pipeline_orchestrator.py generate \ --type airflow \ --source postgres \ --destination snowflake \ --schedule "0 5 * * *" # Validate data quality python scripts/data_quality_validator.py validate \ --input data/sales.parquet \ --schema schemas/sales.json \ --checks freshness,completeness,uniqueness # Optimize ETL performance python scripts/etl_performance_optimizer.py analyze \ --query queries/daily_aggregation.sql \ --e...
OpenClaw Skill for stock data analysis
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