Run comprehensive data health checks on the database, reporting on data quality issues, dead letters, duplicates, and anomalies. Use when you need to assess overall data quality, identify issues requiring attention, or perform daily data quality monitoring.
Core mathematical concepts and theoretical frameworks for statistics
Python and R programming for data analysis, automation, and reproducible analytics
Clean up Tableau workbooks by standardizing captions, adding comments, and organizing into folders.
Analyzes ICU clinical data using the Common Longitudinal ICU data Format (CLIF) and clifpy Python library. Loads and filters CLIF tables (vitals, labs, medications, respiratory support, microbiology) by hospitalization_id and category columns. Computes clinical scores including SOFA, Charlson Comorbidity Index (CCI), and Elixhauser. Creates wide datasets and performs data transformations. Use when working with ICU data, CLIF format, clifpy, clinical scoring, ventilator data, sepsis research, or intensive care analytics.
Statiske analyseverktøy for drift-sjekk, sikkerhetsscan og kodekvalitet. Bruk før commit eller for å finne synkroniseringsproblemer.
Statistical analysis methods, hypothesis testing, and probability for data analytics
Formats markdown tables with fixed-width columns for consistent alignment. Use when creating tables, reformatting existing tables, or ensuring table readability. Triggers on terms like "format table", "align table", "fix table", "table columns".
Visualize audio frequency/time-domain data synchronized with React updates.
Comprehensive toolkit for survival analysis and time-to-event modeling in Python using scikit-survival. Use this skill when working with censored survival data, performing time-to-event analysis, fitting Cox models, Random Survival Forests, Gradient Boosting models, or Survival SVMs, evaluating survival predictions with concordance index or Brier score, handling competing risks, or implementing any survival analysis workflow with the scikit-survival library.
Skill professionnel pour Claude Code permettant d'accéder, télécharger et analyser les données ouvertes françaises via data.gouv.fr. Inclut une librairie Python complète, des exemples de code, et une documentation détaillée des datasets les plus utilisés.
World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics. Expertise in Python (NumPy, Pandas, Scikit-learn), R, SQL, statistical methods, A/B testing, time series, and business intelligence. Includes experiment design, feature engineering, model evaluation, and stakeholder communication. Use when designing experiments, building predictive models, performing causal analysis, or driving data-driven decisions.
Establish neutral baseline metrics for unbiased assessment.
Create sequence views showing temporal flows and user interactions. Uses plain arrows with descriptive labels (no relationship kinds).
Expert-level Power BI, DAX, M language, data modeling, Power Query, report design, and paginated reports
This skill should be used when seeding databases with realistic fake data for development, testing, or staging environments. Supports PostgreSQL, MySQL, SQLite, MongoDB with ORM-based seeding (SQLAlchemy, Django, Prisma) and Faker library for generating realistic test data. Use when the user needs to populate databases with sample data, create test fixtures, or set up development/staging environments with realistic data.
Analyzes data sources and generates Nixtla-compatible schema transformations. Infers column mappings, creates transformation modules for CSV/SQL/Parquet/dbt sources, generates schema contracts, and validates data quality. Activates when user needs data transformation, schema mapping, column inference, or Nixtla format conversion.
Execute data processing workflows defined in YAML configuration files. Supports data loading, transformation, validation, and reporting pipelines.
Automatically detect schema changes and manage EF Core migrations
This skill should be used when ingesting market data from multiple sources (APIs, databases, files) into Zipline-Reloaded. It provides standardized patterns for data normalization, quality checks, and pipeline integration.
Data Lake architecture and management including medallion architecture (bronze/silver/gold zones), data catalog with AWS Glue, partitioning strategies, schema evolution, data quality, governance, cost optimization, S3 lifecycle policies, data retention, compliance, query optimization with Athena, data formats (Parquet, ORC, Avro), incremental processing, CDC patterns, and production best practices for scalable data lakes.
