Data & AI skills

Apache Airflow, dbt, Prefect, Dagster, and modern data orchestration for production data pipelines

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Expert data scientist for advanced analytics, machine learning, and statistical modeling. Handles complex data analysis, predictive modeling, and business intelligence.

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Data analysis best practices with pandas, numpy, matplotlib, seaborn, and Jupyter notebooks.

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Expert guidance for data analysis, visualization, and Jupyter Notebook development with pandas, matplotlib, seaborn, and numpy.

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Apache Spark, Hadoop, distributed computing, and large-scale data processing for petabyte-scale workloads

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Guidelines for data analysis and Jupyter Notebook development with pandas, matplotlib, seaborn, and numpy.

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Best practices and guidelines for Apache Kafka event streaming and distributed messaging

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Statistics, probability, linear algebra, and mathematical foundations for data science

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Design a PostgreSQL-specific schema. Covers best-practices, data types, indexing, constraints, performance patterns, and advanced features

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Build features guided by data insights, A/B testing, and continuous measurement using specialized agents for analysis, implementation, and experimentation.

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SymPy is a Python library for symbolic mathematics that enables exact computation using mathematical symbols rather than numerical approximations.

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Matplotlib is Python's foundational visualization library for creating static, animated, and interactive plots.

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Astropy is the core Python package for astronomy, providing essential functionality for astronomical research and data analysis.

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PostgreSQL database optimization workflow for query tuning, indexing strategies, performance analysis, and production database management.

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Performs numerical and symbolic mathematics using NumPy, SciPy, SymPy, and mpmath. Use when the user needs linear algebra, arrays, optimization, integration, ODEs, symbolic expressions, equation solving, or arbitrary-precision arithmetic.

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Snowflake, BigQuery, Redshift, dimensional modeling, and modern data warehouse architecture

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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.

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Scanpy is a scalable Python toolkit for analyzing single-cell RNA-seq data, built on AnnData. Apply this skill for complete single-cell workflows including quality control, normalization, dimensionality reduction, clustering, marker gene identification, visualization, and trajectory analysis.

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Best practices for SciPy scientific computing, optimization, signal processing, and statistical analysis in Python

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Statsmodels is Python's premier library for statistical modeling, providing tools for estimation, inference, and diagnostics across a wide range of statistical methods.

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スキーマ/データ移行の安全性を framework 非依存で審査する。破壊的スキーマ変更・ロック誘発・backfill・ロールバック可逆性・expand-contract の段階適用を、prisma / typeorm / Rails / Django / Alembic / 生 SQL などに横断適用する。

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Ensure designs define data flow, state ownership, consistency boundaries, and cross-boundary writes to prevent drift and incidents.

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Ensure data model/DB designs cover constraints, integrity, indexes, migrations, rollback, and operational impacts.

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Ensure architecture docs define RPO/RTO, failover paths, data consistency, and DR drillability.

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