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Data validation patterns for the World of Darkness Django application including database constraints, model validators, and atomic transactions. Use when implementing XP/freebie spending transactions, adding database constraints to models, writing clean() validation methods, or ensuring data integrity for character stats.
Expert-level Apache Airflow orchestration, DAGs, operators, sensors, XComs, task dependencies, and scheduling
Use the domino-data Python SDK (dominodatalab-data) for programmatic data access in Domino. Covers DataSourceClient for SQL queries and object storage, DatasetClient for dataset files, TrainingSets for ML data versioning, Feature Store, and VectorDB (Pinecone) integration. Use when querying data sources, downloading datasets, managing training sets, or working with vector databases in Domino.
PostgreSQL streaming replication - setup, monitoring, failover
AI agent performs database administration tasks including backup/restore, monitoring, replication, security hardening, and maintenance operations. Use when managing production databases, troubleshooting performance, or implementing high availability.
Sync delta specs from a change to main specs. Use when the user wants to update main specs with changes from a delta spec, without archiving the change.
Master machine learning, data engineering, AI engineering, MLOps, and prompt engineering. Build intelligent systems from data pipelines to production AI applications with LLMs, agents, and modern frameworks.
Use OpenMetadata SDK and APIs to build integrations, connectors, and automations. Use when querying metadata, creating custom properties, building ingestion pipelines, automating governance workflows, or integrating OpenMetadata with other systems.
Work with distributed computing frameworks in Domino including Apache Spark, Ray, and Dask clusters. Covers cluster configuration, on-demand clusters, choosing between frameworks, PySpark usage, and scaling workloads. Use when processing large datasets, parallel ML training, or running distributed compute jobs.
Replay failed observations after bug fixes or database issues. Shows which observations succeed and fail, helping recover from failures and validate fixes work on real data.
This skill should be used when creating custom Zipline data bundles from CSV, API, or database sources. It provides deterministic patterns for ingesting OHLCV data, registering bundles, and validating data integrity for backtesting.
Extracts and displays JSON data from Apache Avro files. Use this when the user wants to read, convert, or view the contents of an .avro file. Automatically deserializes nested JSON fields for better readability.
Create, run, and manage Domino Jobs - batch executions for scripts, training, and data processing. Covers job configuration, hardware tiers, scheduled jobs (cron), monitoring status, viewing logs, and API-driven execution. Use when running batch workloads, scheduling recurring tasks, or automating training pipelines.
Manage PostgreSQL databases using Postgres MCP. Query data, inspect schemas, analyze table structures, run migrations, debug database issues, and manage test data. Use when working with databases, debugging queries, or validating data integrity.
Create, alter, and validate Snowflake semantic views using the Snowflake CLI (`snow`). Use when asked to build or troubleshoot semantic views or semantic layer definitions with CREATE/ALTER SEMANTIC VIEW, to validate semantic view DDL against Snowflake via the CLI, or to guide Snowflake CLI installation and connection setup.
PostgreSQL DBA skill for schema design review, indexing, query tuning (EXPLAIN/ANALYZE), vacuum/autovacuum, concurrency/locking, partitioning, backup/restore, replication/HA, and safe migrations. Use for tasks like diagnosing slow queries, designing indexes and constraints, and operating Postgres in production.
Kailash DataFlow - zero-config database framework with automatic model-to-node generation. Use when asking about 'database operations', 'DataFlow', 'database models', 'CRUD operations', 'bulk operations', 'database queries', 'database migrations', 'multi-tenancy', 'multi-instance', 'database transactions', 'PostgreSQL', 'MySQL', 'SQLite', 'MongoDB', 'pgvector', 'vector search', 'document database', 'RAG', 'semantic search', 'existing database', 'database performance', 'database deployment', 'database testing', or 'TDD with databases'. DataFlow is NOT an ORM - it generates 11 workflow nodes per SQL model, 8 nodes for MongoDB, and 3 nodes for vector operations.
