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Found 449 Skills
SQL database queries, joins, aggregations, subqueries, and optimization. Use for .sql files and database operations.
Use when querying Outlit customer data via MCP tools (outlit_*). Triggers on customer analytics, revenue metrics, activity timelines, cohort analysis, churn risk assessment, SQL queries against analytics data, or any Outlit data exploration task.
Validates SQL schema files for compliance with internal safety and naming policies.
Use when setting up a production database for Bknd. Covers SQLite file, LibSQL/Turso, Cloudflare D1, PostgreSQL, Neon, Supabase, and Xata configuration.
SQLAlchemy and database patterns for Python. Triggers on: sqlalchemy, database, orm, migration, alembic, async database, connection pool, repository pattern, unit of work.
Use when working with SQLite databases in Bun. Covers Bun's built-in SQLite driver, database operations, prepared statements, and transactions with high performance.
Design optimized database schemas for SQL and NoSQL databases including tables, relationships, indexes, and constraints. Creates ERD diagrams, migration scripts, and data modeling best practices. Use when users need database design, schema optimization, or data architecture planning.
Create Databricks AI/BI dashboards. CRITICAL: You MUST test ALL SQL queries via execute_sql BEFORE deploying. Follow guidelines strictly.
Kotlin Multiplatform development context. Apply when working with shared/ or commonMain/, expect/actual declarations, .kt files in multiplatform modules, Koin, SQLDelight, Ktor, Compose Multiplatform.
MUST USE when installing chv, setting up local ClickHouse development, or running ClickHouse locally. Contains 5 guides covering chv CLI installation, local project initialization, running a local server, executing SQL from files, and migrating to cloud. Always read relevant guide files and cite them in responses.
Expert guidance for SQLModel - the Python library combining SQLAlchemy and Pydantic for database models. Use when (1) creating database models that work as both SQLAlchemy ORM and Pydantic schemas, (2) building FastAPI apps with database integration, (3) defining model relationships (one-to-many, many-to-many), (4) performing CRUD operations with type safety, (5) setting up async database sessions, (6) integrating with Alembic migrations, (7) handling model inheritance and mixins, or (8) converting between database models and API schemas.
Master data engineering, ETL/ELT, data warehousing, SQL optimization, and analytics. Use when building data pipelines, designing data systems, or working with large datasets.