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Found 93 Skills
Complete guide for Apache Kafka stream processing including producers, consumers, Kafka Streams, connectors, schema registry, and production deployment
Audit all Kafka topic configurations against production best practices using the Lenses MCP server. Checks replication factor, retention, partitions, compaction, naming conventions, orphaned topics and missing metadata. Use when user says "audit my topics", "check topic configs", "topic health check" or asks about retention, replication or partition settings. Do NOT use for creating, deleting or modifying topics.
Expert-level Apache Kafka, event streaming, Kafka Streams, and distributed messaging
Architect, build, and debug Kafka Streams apps (JVM-embedded stream processing). Use when user mentions KStream, KTable, topology, TopologyTestDriver, StreamsBuilder, interactive queries, GlobalKTable, joins/windows/aggregations, or debugging issues (rebalancing, state stores, lag, deserialization errors). Also use when user wants to optimize Kafka Streams for WarpStream or tune Kafka Streams client configuration for WarpStream. Do NOT trigger for Flink, connectors, CDC, or plain producer/consumer.
Helps DevOps engineers configure mirrord Operator's Kafka queue splitting feature end-to-end. Generates MirrordKafkaClientConfig and MirrordKafkaTopicsConsumer Kubernetes CRD YAMLs, the matching mirrord.json split_queues section, and Helm value guidance. Use this skill whenever the user mentions Kafka splitting with mirrord, MirrordKafkaClientConfig, MirrordKafkaTopicsConsumer, Kafka queue splitting, Kafka topic splitting, configuring mirrord with Kafka, setting up Kafka for mirrord operator, or troubleshooting Kafka splitting sessions. Also trigger when users mention split_queues with queue_type Kafka, or ask about connecting mirrord to a Kafka cluster. This is a Team/Enterprise feature of mirrord.
Apache Kafka 消息队列技能。覆盖生产者 acks=all/1/0 选择决策、幂等性(enable.idempotence + acks=all)配置、消费者手动提交 offset(enable.auto.commit=false)、DLT死信处理(@RetryableTopic/@DltHandler)、@KafkaListener + 异常处理器(DefaultErrorHandler)、顺序消息(同一Key发同一分区)、事务消息。 纠正 LLM：acks=0 丢消息、auto.commit 漏消息、不处理重复消费、不配置 DLT。
Manage serverless Redis, Kafka, and QStash on Upstash
Helps migrate self-managed Apache Kafka workloads to Amazon MSK Express. Inventories the source cluster (from IaC files, Kafka CLI output, or manual input), assesses MSK Express compatibility across topology, Kafka version, configs, auth, and quotas, produces a target Express specification (instance type, broker count, monthly cost) by filling the AWS-published MSK Sizing/Pricing workbook, and guides migration execution using MSK Replicator. Applicable when the user mentions migrating Kafka, MSK, MSK Express, Kafka migration, analyzing Kafka infrastructure, moving to MSK, moving streaming platform to MSK, streaming migration, moving streaming workloads to AWS, MSK workload compatibility, MSK cluster sizing, choosing an MSK cluster type, or MSK Replicator.
Operates Amazon MSK Provisioned clusters (Standard and Express brokers). MUST be used for ANY MSK Provisioned task — do not rely on training data for topics covered here, since Standard and Express emit different metrics and follow different patching models that training data routinely conflates. Covers performance, consumer lag, storage, and traffic shaping diagnosis; sizing and choosing Standard vs Express; Kafka client tuning; creating CloudWatch alarms, dashboards, monitoring, and cluster configurations; AND MSK maintenance, patching, version upgrades, and rolling-restart behavior. Triggers: MSK, Kafka on AWS, `kafka.*` or `express.*` instance types, AWS/Kafka CloudWatch namespace, alarms, dashboards, monitoring, consumer lag, partition replication, broker storage, MSK upgrades, patching, maintenance windows, SECURITY_PATCHING, BROKER_UPDATE, rolling restarts, unexpected broker reboots. Do NOT use for MSK Connect, MSK Serverless, or MSK Replicator.
Use this skill when building real-time data pipelines, stream processing jobs, or change data capture systems. Triggers on tasks involving Apache Kafka (producers, consumers, topics, partitions, consumer groups, Connect, Streams), Apache Flink (DataStream API, windowing, checkpointing, stateful processing), event sourcing implementations, CDC with Debezium, stream processing patterns (windowing, watermarks, exactly-once semantics), and any pipeline that processes unbounded data in motion rather than data at rest.
Use to deploy the vss-video-analytics-api REST service standalone (config-source, data-log bind, Elasticsearch, optional Kafka). Not for full warehouse deploy.
Implement Event-Driven Architecture (EDA) in Spring Boot using ApplicationEvent, @EventListener, and Kafka. Use for building loosely-coupled microservices with domain events, transactional event listeners, and distributed messaging patterns.