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Found 31 Skills
Analyze a Materialize environment for health, performance, and optimization opportunities using the MCP Developer endpoint. Use this skill when someone wants to check environment health, investigate performance issues, troubleshoot stale materialized views, diagnose memory pressure, audit resource utilization, or get optimization recommendations. Trigger this even if the user just says "check my environment", "why is my MV stale", "why is my cluster slow", or "what can I optimize".
Materialize documentation for SQL syntax, data ingestion, concepts, and best practices. Use when users ask about Materialize queries, sources, sinks, views, or clusters.
Build read models and projections from event streams. Use when implementing CQRS read sides, building materialized views, or optimizing query performance in event-sourced systems.
Guide for creating GreptimeDB flow tasks, for generates materialized view for continuous aggregation.
Using the mz-deploy CLI to manage a declarative SQL project for Materialize — project layout and the stage/wait/promote deployment lifecycle. Use this skill whenever the user is working in an mz-deploy project (a directory containing project.toml), asks how to deploy SQL changes to Materialize safely, or mentions mz-deploy, project.toml, profiles.toml, types.lock, staging deployments, deploy IDs, `mz-deploy stage`, `mz-deploy promote`, `mz-deploy abort`, `mz-deploy apply`, `SET api = stable`, stable API schemas, replacement materialized views, EXECUTE UNIT TEST, or per-profile SQL file overrides (`name#profile.sql`). Also trigger when a user needs to roll back a Materialize deployment or resolve a deployment conflict.
Use when reading or editing .sql files in an mz-deploy project (a directory with project.toml) — resolve object references, columns, and types with the LSP tool instead of grepping. Covers go-to-definition, hover, and document/workspace symbols for Materialize SQL.
Provides authoritative compatibility checks, pricing estimates, connection troubleshooting, pre-warming guidance, and infrastructure mutations for Amazon Keyspaces (for Apache Cassandra). Covers LWT/batch operations, secondary indexes, materialized views, capacity modes, TTL, PITR, CDC, auto-scaling, multi-region keyspaces, UDTs, nodetool diagnostics parsing, SQL-to-Cassandra migration, and Cassandra-to-Keyspaces migration scenarios. Agents frequently produce incomplete or incorrect answers about Keyspaces feature support without this skill loaded.
129 practical Oracle Database and Oracle Container Registry reference guides covering SQL/PL/SQL development, performance tuning (AWR, ASH, explain plan, indexes, wait events, memory), security (TDE, VPD, auditing, network), administration (RMAN, Data Guard, undo/redo, users), monitoring, architecture (RAC, CDB/PDB, Exadata, In-Memory, OCI), DevOps (Liquibase, Flyway, utPLSQL, EBR), migrations from Postgres/MySQL/SQL Server/MongoDB/Snowflake/Redshift/DB2, PL/SQL development (packages, cursors, collections, unit testing, debugging), Oracle features (AQ, DBMS_SCHEDULER, materialized views, APEX), SQLcl (basics, scripting, Liquibase, MCP server, CI/CD), ORDS (architecture, authentication, AutoREST, REST API design, PL/SQL gateway), and Oracle Container Registry images. Use for any Oracle DB question, ORA- errors, DBMS_ packages, v$ views, Oracle tooling, ORDS REST APIs, SQLcl commands, or Oracle container images. Always consult this skill before answering Oracle-specific questions.
Use this skill when designing data warehouses, building star or snowflake schemas, implementing slowly changing dimensions (SCDs), writing analytical SQL for Snowflake or BigQuery, creating fact and dimension tables, or planning ETL/ELT pipelines for analytics. Triggers on dimensional modeling, surrogate keys, conformed dimensions, warehouse architecture, data vault, partitioning strategies, materialized views, and any task requiring OLAP schema design or warehouse query optimization.
Use this skill whenever working with QuestDB — a high-performance time-series database. Trigger on any mention of QuestDB, time-series SQL with SAMPLE BY, LATEST ON, ASOF JOIN, ILP ingestion, or the questdb Python/Go/Java/Rust/.NET client libraries. Also trigger when writing Grafana queries against QuestDB, creating materialized views for time-series rollups, working with order book or financial market data in QuestDB, or any SQL that involves designated timestamps or time-partitioned tables. QuestDB extends SQL with unique time-series keywords — standard PostgreSQL or MySQL patterns will fail. Always read this skill before writing QuestDB SQL to avoid hallucinating incorrect syntax.
Analyze ClickHouse table structure, partitioning, ORDER BY keys, materialized views, and identify schema design anti-patterns. Use for table design issues and optimization.
Set up end-to-end Change Data Capture (CDC) pipelines on Confluent Cloud using Debezium source connectors, Flink for transformation, and Tableflow for data lake integration. Supports JSON_SR, Avro, and Protobuf formats. Handles schemaless topics (plain JSON without SR) and multi-event topics. This skill handles the complete workflow from database to Iceberg/Delta tables. Use this skill when users want to capture database changes and materialize them into Iceberg or Delta Lake tables via Confluent Cloud Tableflow. Trigger phrases include "CDC to Tableflow", "database to Iceberg", "database to Delta Lake", "stream database changes to data lake", "set up Tableflow pipeline", "schemaless topic to Tableflow", or "multi-event topic to Iceberg". Do NOT trigger for general CDC, Debezium, or database replication requests that do not involve Tableflow or Iceberg/Delta Lake as the destination.