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Found 75 Skills
Use to deploy the vss-video-analytics-api REST service standalone (config-source, data-log bind, Elasticsearch, optional Kafka). Not for full warehouse deploy.
Expert in Apache Kafka, Event Streaming, and Real-time Data Pipelines. Specializes in Kafka Connect, KSQL, and Schema Registry.
Set up Kafka-based event-driven microservices with Platformatic Watt. Use when users ask about: - "kafka", "event-driven", "messaging" - "kafka hooks", "kafka webhooks" - "kafka producer", "kafka consumer" - "dead letter queue", "DLQ" - "request response pattern" with Kafka - "migrate from kafkajs", "kafkajs migration", "replace kafkajs" Covers @platformatic/kafka, @platformatic/kafka-hooks, consumer lag monitoring, and OpenTelemetry instrumentation.
Audit Kafka security configuration across the codebase and live cluster using the Lenses MCP server. Checks authentication (SASL), encryption (SSL/TLS), authorisation (ACLs), secrets management and environment tier mismatches. Use when user says "audit Kafka security", "check security config", "is my cluster secure" or asks about authentication, encryption or credentials. Do NOT use for configuring certificates, creating SASL users or setting up ACLs.
Expert-level Apache Kafka, event streaming, Kafka Streams, and distributed messaging
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.
Build event streaming and real-time data pipelines with Kafka, Pulsar, Redpanda, Flink, and Spark. Covers producer/consumer patterns, stream processing, event sourcing, and CDC across TypeScript, Python, Go, and Java. When building real-time systems, microservices communication, or data integration pipelines.
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.
Comprehensive guide to Spark Structured Streaming for production workloads. Use when building streaming pipelines, working with Kafka ingestion, implementing Real-Time Mode (RTM), configuring triggers (processingTime, availableNow), handling stateful operations with watermarks, optimizing checkpoints, performing stream-stream or stream-static joins, writing to multiple sinks, or tuning streaming cost and performance.
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.
Use this skill when building real-time or near-real-time data pipelines. Covers Kafka, Flink, Spark Streaming, Snowpipe, BigQuery streaming, materialized views, and batch-vs-streaming decisions. Common phrases: "real-time pipeline", "Kafka consumer", "streaming vs batch", "low latency ingestion". Do NOT use for batch integration patterns (use integration-patterns-skill) or pipeline orchestration (use data-orchestration-skill).
Review Kafka schema changes (Avro, Protobuf, JSON Schema) for compatibility and evolution best practices using the Lenses MCP server. Detects breaking changes, missing defaults, schema drift and naming issues. Use when user says "review schema changes", "check schema compatibility", "will this schema break consumers" or asks about schema evolution. Do NOT use for creating new schemas from scratch or registering them in the cluster.