convex-development
Convex function patterns, validators, queries with indexes, mutations with auth checks, and real-time data. Use when writing Convex queries, mutations, actions, schema changes, or debugging Convex code.
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Convex function patterns, validators, queries with indexes, mutations with auth checks, and real-time data. Use when writing Convex queries, mutations, actions, schema changes, or debugging Convex code.
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 va...
Apache Airflow DAG development with TaskFlow API, Google Cloud operators (BigQuery, GCS), dbt integration, and dynamic DAG generation. Use when creating or modifying Airflow DAGs, implementing data pipeline orc...
BigQuery query optimization, feature engineering, and BigQuery ML models for recommendation systems. Use when writing optimized SQL queries, implementing partitioning/clustering, creating materialized views, en...
Jenkins CI/CD pipelines for data platforms with Kubernetes pod agents. Use when creating Jenkinsfiles, implementing parallel linting stages (Ruff, Black, Mypy), DAG validation with DagBag, security scanning (Ba...
guidance on how to properly do data migrations in Convex
PostgreSQL problem-solving patterns for production systems. Use when: query optimization, concurrency issues, zero-downtime migrations, scaling decisions, debugging slow queries, implementing patterns from data...
Creates Robot Framework test cases for uploading files to SnapLogic SLDB. Use when the user wants to upload files (JSON, CSV, expression libraries, pipelines, JAR files, etc.), needs to know which destination p...
Creates Robot Framework test cases for uploading files to SnapLogic SLDB. Use when the user wants to upload files (JSON, CSV, expression libraries, pipelines, JAR files, etc.), needs to know which destination p...
Supabase development standards. Triggers when working with Supabase projects, Row Level Security, real-time subscriptions, or Edge Functions.
DBA Architect Agent. 스키마 설계, 정규화, 데이터 모델링을 담당합니다.
Executive CDO Agent. 데이터 전략, 데이터 거버넌스, AI/ML 전략을 담당합니다.
Data Engineer Agent. ETL 파이프라인, 데이터 웨어하우스, 데이터 레이크 구축을 담당합니다.
Use when working with wcdb
Generate real Cassandra 5.0 test data using Docker containers, export SSTables with proper directory structure, validate parsing against sstabledump, and manage test datasets. Use when working with test data ge...
Guide parsing of Cassandra 5.0+ SSTable components (Data.db, Index.db, Statistics.db, Summary.db, TOC) with compression support (LZ4, Snappy, Deflate). Use when working with SSTable files, binary format parsing...
Implement and deserialize all CQL types including primitives (int, text, timestamp, uuid, varint, decimal), collections (list, set, map), tuples, UDTs (user-defined types), and frozen types. Use when working wi...
Expert guidance for designing, optimizing, and maintaining database schemas for SQL and NoSQL systems. Use when creating new databases, optimizing existing schemas, planning migrations, implementing security po...
Use when "data pipelines", "ETL", "data warehousing", "data lakes", or asking about "Airflow", "Spark", "dbt", "Snowflake", "BigQuery", "data modeling"
Use when "Polars", "fast dataframe", "lazy evaluation", "Arrow backend", or asking about "pandas alternative", "parallel dataframe", "large CSV processing", "ETL pipeline", "expression API"
Use when "Dask", "parallel computing", "distributed computing", "larger than memory", or asking about "parallel pandas", "parallel numpy", "out-of-core", "multi-file processing", "cluster computing", "lazy eval...
SQL and database best practices including query optimization, indexing, and schema design.
MongoDB best practices including schema design, indexing, and query optimization.
Create and manage database migrations with reversible up/down methods, zero-downtime deployment strategies, and proper schema versioning. Use this skill when creating database migration files, altering table sc...
Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.