Data Quality Frameworks
Implement data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rules, or establishing d
Hand-picked AI agent skills for data analysis. SQL, exploratory analysis, and insight narratives from raw data. Every entry is community-rated and free to copy — grab one and get to work.
Implement data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rules, or establishing d
Build financial models, backtest trading strategies, and analyze market data. Implements risk metrics, portfolio optimization, and statistical arbitrage. Use PROACTIVELY for quanti
Pulls recent production logs filtered for errors, warnings, and anomalies. Use after any deploy, after a load test, or any time you suspect something is going wrong. Treats logs as
Expert in vector databases, embedding strategies, and semantic search implementation. Masters Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation
Prepare, format, and validate datasets for supervised fine-tuning and preference training. Use when converting raw data into training format, applying chat templates, configuring s
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