numpy-datetime
Date and time handling with datetime64 and timedelta64, including business day offsets and naive time parsing. Triggers: datetime64, timedelta64, busday, time series, naive time.
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Date and time handling with datetime64 and timedelta64, including business day offsets and naive time parsing. Triggers: datetime64, timedelta64, busday, time series, naive time.
Pytest testing patterns for data pipelines, Airflow DAGs, and BigQuery queries. Use when writing unit tests for DAG tasks, integration tests with BigQuery, property-based tests with Hypothesis, data contract validation with Pydantic/JSON schemas, performance tests with pytest-benchmark, or snapshot/visual regression tests for data outputs.
GLOWマスタデータCSVの検証スキル。作成したCSVファイルがDB投入可能か、server/client実装と整合性があるかをテンプレート、DBスキーマと照合してチェックします。マスタデータ、CSV検証、バリデーション、チェックで使用します。
Monitor and troubleshoot dual-pipeline data collection systems on GCP. This skill should be used when checking pipeline health, viewing logs, diagnosing failures, or monitoring long-running operations for data collection workflows. Supports Cloud Run Jobs (batch pipelines) and VM systemd services (real-time streams).
Strategies for managing data structures, versioning, and evolutions across databases and event streams.
Analyzes legacy COBOL programs and JCL jobs to assist with migration to modern Java applications. Extracts business logic, identifies dependencies, generates migration reports, and creates Java implementation strategies. Use when working with mainframe migration, COBOL analysis, legacy system modernization, JCL workflows, or when users mention COBOL to Java conversion, analyzing .cbl/.CBL/.cob files, working with copybooks, or planning Java service implementations from COBOL programs.
Design complete database schemas with tables, relationships, constraints, and indexes for Supabase. Triggers when user describes data models, entities, or requests schema design.
Validates data against specified rules including required field checks, email format validation, and numeric type verification. Use when the user needs to verify data integrity, validate form inputs, or check data against business rules.
See the main Data Validation Rules skill for comprehensive coverage of data quality rule implementation.
Understanding the Prisma schema, core entities, and database operations.
Use when "data pipelines", "ETL", "data warehousing", "data lakes", or asking about "Airflow", "Spark", "dbt", "Snowflake", "BigQuery", "data modeling"
Apply cleaning and filtering actions based on data quality decisions and generate filtered log artefacts.
Implementing comprehensive validation rules across database, application, and pipeline layers to ensure data integrity.
Database schema validation tools - SQL syntax checking, constraint validation, naming convention enforcement, and schema integrity verification. Use when validating database schemas, checking migrations, enforcing naming conventions, verifying constraints, or when user mentions schema validation, migration checks, database best practices, or PostgreSQL validation.
World-class 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, or implementing data governance.
Analyze ClickHouse external dictionaries including configuration, memory usage, reload status, and performance. Use for dictionary issues and load failures.
Query signals pipeline OTLP logs via the loglens Athena-backed CLI. Use when you need to search or inspect signals logs, run opinionated filters (logs/top-errors/schema), or execute raw SQL against the compacted or flat log tables in dev/stage/prod. Triggers include requests mentioning loglens, signals logs, OTLP logs, telemetry alerts, push-server logs, or Athena log queries.
SpatiaLite database operations for aviation data. Use when building spatial indexes, running proximity queries, importing airspace GeoJSON, or performing geometric calculations on aviation features like finding airports within radius, airspace containment, or obstacle corridor searches.
Trace data flow across files from source to sink. Use for cross-file taint analysis, understanding how user input reaches dangerous sinks, and documenting vulnerability paths.