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Foundational SQL patterns for query writing, schema design, and dialect differences. Use when writing SQL queries, designing database schemas, understanding SQL syntax across PostgreSQL/MySQL/SQLite, or preparing SQL for conversion to other query languages. This is a meta-skill for SQL derivatives.
Author SEA-DSL specifications with proper CQRS annotations, validation, and namespace conventions. Use when creating .sea files with entities, flows, and policies that require @cqrs, @tx, @outbox, and @idempotency annotations following the Flow-only CQRS tagging pattern.
Scale PostgreSQL - partitioning, connection pooling, high availability
Docker, Kubernetes, container orchestration, and cloud-native deployment for data applications
校验页面实例与Schema定义的一致性（类型/约束/安全），并给出可发布结论。保存页面、预发布检查或批量体检时调用。
Create, alter, and validate Snowflake semantic views using Snowflake CLI (snow). Use when asked to build or troubleshoot semantic views/semantic layer definitions with CREATE/ALTER SEMANTIC VIEW, to validate semantic-view DDL against Snowflake via CLI, or to guide Snowflake CLI installation and connection setup.
AI agent builds full-stack applications with Supabase PostgreSQL, authentication, Row Level Security, Edge Functions, and real-time subscriptions. Use when building apps with Supabase, implementing RLS policies, or setting up Supabase Auth.
PostgreSQL database optimization combining institutional knowledge with live analysis
Build ETL pipelines, data warehouses, and streaming architectures. Implements Spark jobs, Airflow DAGs, and Kafka streams. Use PROACTIVELY for data pipeline design or analytics infrastructure.
Bulk data operations with Excel import/export, batch processing, and validation for ABP Framework. Use when: (1) implementing bulk import/update from Excel/CSV, (2) batch processing with progress tracking, (3) file upload with validation, (4) bulk database operations with InsertManyAsync/UpdateManyAsync.
Use this agent when you need to review database migrations, data models, or any code that manipulates persistent data. This includes checking migration safety, validating data constraints, ensuring transaction boundaries are correct, and verifying that referential integrity and privacy requirements are maintained. <example>Context: The user has just written a database migration that adds a new column and updates existing records. user: "I've created a migration to add a status column to the orders table" assistant: "I'll use the data-integrity-guardian agent to review this migration for safety and data integrity concerns" <commentary>Since the user has created a database migration, use the data-integrity-guardian agent to ensure the migration is safe, handles existing data properly, and maintains referential integrity.</commentary></example> <example>Context: The user has implemented a service that transfers data between models. user: "Here's my new service that moves user data from the legacy_users table to
Transforms forecasting experiments into production-ready inference pipelines with Airflow, Prefect, or cron orchestration. Generates ETL tasks, monitoring, error handling, and deployment configs. Activates when user needs to deploy forecasts to production, schedule batch inference, operationalize models, or create production pipelines.
Filter and search event datasets (logs) using OPAL. Use when you need to find specific log events by text search, regex patterns, or field values. Covers contains(), tilda operator ~, field comparisons, boolean logic, and limit for sampling results. Does NOT cover aggregation (see aggregating-event-datasets skill).
Complete guide to ActiveRecord query optimization, associations, scopes, and PostgreSQL-specific patterns. Use this skill when writing database queries, designing model associations, creating migrations, optimizing query performance, or debugging N+1 queries and grouping errors. Trigger keywords: database, models, associations, validations, queries, ActiveRecord, scopes, migrations, N+1, PostgreSQL, indexes, eager loading
Advanced distributed event patterns for ABP microservices including idempotent handlers, cross-tenant events, event sourcing lite, and saga patterns. Use when: (1) implementing event handlers across services, (2) ensuring idempotent event processing, (3) cross-tenant event handling, (4) designing event-driven architectures.
Comprehensive Python data engineering patterns for AWS Data Lake, including PySpark, Pandas, Apache Airflow, AWS Glue, ETL pipelines, data quality, schema management, performance optimization, FastAPI services, streaming with Kafka/Kinesis, data validation with Great Expectations, testing strategies, error handling, logging, and production deployment on AWS EMR and Glue.
Manage Logstash pipelines deployed in OpenShift using ConfigMaps. Use for pipeline configuration, deployment, troubleshooting, and maintenance of Logstash in containerized OpenShift environments.
Tool for managing Neon Postgres databases. Use for listing projects, inspecting schema, and running queries.
