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Found 60 Skills
Answer questions using the Tenzir documentation. Use whenever the user asks about TQL syntax, pipeline operators, functions, data parsing or transformation, normalization, OCSF mapping, enrichment, lookup tables, contexts, packages, nodes, platform setup, deployment, configuration, integrations with tools like Splunk, Kafka, S3, Elasticsearch, or any other Tenzir feature. Also use when the user asks how to collect, route, filter, aggregate, or export security data with Tenzir, or needs help writing or debugging TQL pipelines, even if they don't mention 'Tenzir' explicitly but are clearly working in a Tenzir context.
testcontainers-python specialist. Covers all container modules (PostgreSQL, MySQL, MongoDB, Redis, Kafka, RabbitMQ, MinIO, Elasticsearch, LocalStack), GenericContainer, wait strategies, Docker Compose, networks, pytest fixtures, and CI/CD integration. USE WHEN: user mentions "testcontainers", "docker in tests", "real database in tests", "test with real postgres/redis/kafka", asks about container fixtures or Docker-based testing. DO NOT USE FOR: Spring Boot testcontainers (Java) - use `spring-boot-integration`; Mocking HTTP - use `fastapi-testing`; Pure pytest patterns - use `pytest`
Creates Robot Framework test cases for SnapLogic account creation. Use when the user wants to create accounts (Oracle, PostgreSQL, Snowflake, Kafka, S3, etc.), needs to know what environment variables to configure, or wants to see account test case examples.
RabbitMQ message broker with AMQP protocol. Covers exchanges, queues, bindings, and messaging patterns. Use for reliable message delivery and complex routing scenarios. USE WHEN: user mentions "rabbitmq", "amqp", "exchanges", "routing patterns", "topic exchange", "fanout", asks about "message routing", "work queues", "request/reply", "flexible routing" DO NOT USE FOR: high-throughput streaming - use `kafka` or `pulsar`; cloud-native - use `nats`; AWS-native - use `sqs`; JMS required - use `activemq`; simple pub/sub - use `redis-pubsub`
Amazon SQS managed message queue service. Covers standard and FIFO queues, dead-letter queues, and integration patterns. Use for AWS-native serverless and microservices architectures. USE WHEN: user mentions "sqs", "aws queues", "fifo queue", "lambda trigger", "sns to sqs", asks about "aws messaging", "serverless queues", "standard queue", "visibility timeout" DO NOT USE FOR: event streaming - use `kafka` or AWS Kinesis; Azure-native - use `azure-service-bus`; GCP-native - use `google-pubsub`; on-premise - use `rabbitmq` or `activemq`; complex routing - use `rabbitmq`
Use this skill when designing event-driven systems, implementing event sourcing, applying CQRS patterns, selecting message brokers, or reasoning about eventual consistency. Triggers on tasks involving Kafka, RabbitMQ, event stores, command-query separation, domain events, sagas, compensating transactions, idempotency, message ordering, and any architecture where components communicate through asynchronous events rather than direct synchronous calls.
Use when you need to implement acceptance tests from a Gherkin .feature file for Spring Boot applications — including finding scenarios tagged @acceptance, implementing happy path tests with TestRestTemplate, @SpringBootTest, Testcontainers with @ServiceConnection for DB/Kafka, and WireMock for external REST stubs. Requires .feature file in context. Part of the skills-for-java project
Use when you need to implement acceptance tests from a Gherkin .feature file for framework-agnostic Java (no Spring Boot, Quarkus, Micronaut) — finding @acceptance scenarios, happy path with RestAssured, Testcontainers for DB/Kafka, WireMock for external REST. Requires .feature file in context. Part of the skills-for-java project
Diagnose ClickHouse INSERT performance, batch sizing, part creation patterns, and ingestion bottlenecks. Use for slow inserts and data pipeline issues.
Automatically discover protocol skills when working with HTTP, TCP, UDP, QUIC, and network protocols
Confluent integration. Manage data, records, and automate workflows. Use when the user wants to interact with Confluent data.
Use when the user asks to document an implemented feature. Analyze the diff from the base branch, infer the feature boundary and name, and generate behavioral feature documentation under docs/features/.