Help us improve
Share bugs, ideas, or general feedback.
Share bugs, ideas, or general feedback.
Share bugs, ideas, or general feedback.
By lensesio
Audits Kafka clusters for security misconfigurations, topic compliance, connector health, consumer lag, schema compatibility, and performance anti-patterns via the Lenses MCP server, with remediation recommendations.
npx claudepluginhub lensesio/agentic-engineering-for-apache-kafka --plugin kafka-skillsReview Kafka Connect connector configurations for common misconfigurations using the Lenses MCP server. Checks error handling, DLQ setup, converters, transforms, task count and task health. Use when user says "review connectors", "check connector configs", "why is my connector failing" or asks about Kafka Connect configuration. Do NOT use for creating, deploying or controlling connectors.
Analyse Kafka consumer group lag using the Lenses MCP server. Diagnoses lag causes (throughput bottlenecks, rebalancing, partition skew, stalled consumers) and suggests remediation. Use when user says "check consumer lag", "why are consumers slow", "lag report" or asks about consumer group health or offset progress. Do NOT use for resetting offsets or managing consumer groups.
Review dead letter queue implementations for completeness using the Lenses MCP server. Checks DLQ topic existence, configuration, monitoring, metadata preservation, retry logic, reprocessing paths and connector DLQ alignment. Use when user says "review dead letter queues", "check DLQ setup", "DLQ audit" or asks about error handling, message failures or reprocessing. Do NOT use for reprocessing DLQ messages or managing consumer offsets.
Review Kafka producer and consumer performance configurations in both the live cluster (via Lenses MCP) and the codebase. Flags un-tuned defaults, anti-patterns and missing best practices. Use when user says "review Kafka performance", "check producer configs", "tune Kafka settings" or asks about throughput, batching or compression. Do NOT use for cluster sizing or capacity planning.
Scaffold a production-ready Python Kafka producer and consumer using `confluent-kafka-python`, with Schema Registry, graceful shutdown, idempotent producer, tests and a complete project layout. Discovers the target topic, partition count and registered JSON Schema directly from the live cluster via any attached Kafka MCP server (Lenses MCP, Confluent's MCP, Aiven's, custom) before asking the user. Use when user says "build a Python Kafka consumer", "scaffold a Kafka client in Python", "add Kafka to my Python service", "consume from `<topic>` in Python" or "write a Kafka producer". Do NOT use for Kafka Connect connectors, schema-evolution reviews or non-Python clients.
Share bugs, ideas, or general feedback.
Own this plugin?
Verify ownership to unlock analytics, metadata editing, and a verified badge.
Sign in to claimOwn this plugin?
Verify ownership to unlock analytics, metadata editing, and a verified badge.
Sign in to claimBased on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
Interactive YAML config and Bloblang authoring for Redpanda Connect
Message queues and distributed systems expertise. Master queue theory, RabbitMQ, SQS, Kafka, async processing, backpressure, and distributed system patterns.
Etcd cluster health monitoring and performance analysis utilities
Kubernetes cluster efficiency analysis: resource utilization, Karpenter, OOM, workloads
Claude Code skill pack for ClickHouse (24 skills)
Skills for streaming application developers, covering Kafka and Flink client libraries and Schema Registry
A collection of agent skills that turn AI agents and coding tools such as Claude Code and Cursor into Kafka-specialised engineering assistants. Audit topic configurations, diagnose consumer lag, review schema changes, review connectors and DLQs, catch security misconfigurations and tune performance, all from a single prompt instead of 15 minutes of manual exploration or investigation.
Maintained by Lenses.io, the team that pioneered the developer experience for Apache Kafka. Agentic engineering has shifted what that means. Making sure an AI agent knows how to handle streaming data is now part of the job.
A Kafka MCP server gives agents access to your live cluster: topics, consumer groups, connectors, schemas and metrics. The skills in this repository teach agents expertise: best practices, audit thresholds, remediation playbooks and standard workflows. Together they enable AI-powered Kafka engineering where the agent doesn't just read your cluster, it knows what to look for and how to fix it.
Without skills, agents are confident generalists. They will write a consumer for the orders topic that compiles and runs but does not handle deserialization errors properly, set up DLQs correctly, or partition consumption sensibly for the topic's layout. Skills close the gap between code that runs in a demo and code that holds up in production.
These skills follow the Anthropic open standard for skills, so they are portable across Claude Code, Cursor, Claude.ai and the Claude Messages API. They are MCP-agnostic by design: every skill in this repo is tested against Lenses MCP Server (the recommended setup), but will work with any Kafka MCP server that exposes similar tools.
The quickest way to try the skills end-to-end is with the free Lenses Community Edition, which ships with Lenses HQ, a remote MCP Server and a pre-configured single-broker Kafka cluster with demo data.
| Skill | Invocation | Description | Frequency |
|---|---|---|---|
| Topic Audit | /kafka-topic-audit | Audits topic configs against best practices: replication factor, retention, partitions, compaction, naming conventions, orphaned topics and missing metadata. | Daily/weekly |
| Consumer Lag | /kafka-consumer-lag | Analyses consumer group lag and diagnoses root causes (throughput bottlenecks, rebalancing, partition skew, stalled consumers) with remediation suggestions. | Daily/on-incident |
| Perf Review | /kafka-perf-review | Reviews producer/consumer performance configs in both the live cluster and the codebase. Flags un-tuned defaults, anti-patterns and missing best practices. | Per-change |
| Schema Review | /kafka-schema-review | Reviews schema changes (Avro, Protobuf, JSON Schema) for compatibility, breaking changes, missing defaults, naming issues and schema drift. | Per-PR |
| Security Audit | /kafka-security-audit | Audits authentication (SASL), encryption (SSL/TLS), secrets management and environment-tier mismatches across codebase and cluster. | Monthly/pre-deploy |
| Connector Review | /kafka-connector-review | Reviews Kafka Connect configurations: error handling, DLQ setup, converters, transforms, task count and task health. | Per-change |
| DLQ Review | /kafka-dlq-review | Reviews dead letter queue completeness: topic config, monitoring, metadata preservation, retry logic, reprocessing paths and connector DLQ alignment. | Periodic |
| Python Client | /kafka-python-client | Scaffolds a production-ready Python Kafka producer and consumer using confluent-kafka-python, with Schema Registry, graceful shutdown, idempotent producer and tests. Discovers the target topic, partition count and registered schema from the live cluster via MCP before asking. | Per-project |
Every skill is implemented for both Claude Code and Cursor from a single source of truth at the repo root. The repository itself is the plugin.