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.
请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求,再添加到工作流。
SkillHub CLI
skillhub install alirezarezvani/senior-data-engineer打开来源仓库,查看说明和兼容环境后,按来源提供的命令或步骤安装。
将来源链接提供给你的 Agent,让它先读取说明、核对依赖,并向你说明将执行的安装步骤。
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...
这些信息来自收录时的来源分析,可能滞后;使用前请核对最新来源文件。
# 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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