A collection of AI skills for developing data streaming applications and pipelines with Confluent. These skills help developers using coding assistants quickly build production-ready Kafka producers, Flink applications, and real-time data pipelines by providing guided assistance and code generation. Each skill promotes developer best practices, including proper Schema Registry usage, security configuration, and error handling.
Important: Skill performance may vary based on the underlying language models, which are continuously evolving and nondeterministic in nature. All outputs and code modifications generated by these skills should be carefully reviewed and tested by developers, especially for production applications.
/plugin marketplace add confluentinc/agent-skills
/plugin install streaming-skills-plugin@confluent-agent-skillsThis lets you pick specific skills to install and supports most agents.
npx skills add confluentinc/agent-skillsIf you are using IBM Bob, you must copy/paste your skill into your ~/.bob/skills folder in order for the skill to load on startup.
- Install the skills using one of the methods above
- Open a project where you want to build streaming applications
- Ask your AI agent (e.g., Claude or Cursor) to help with Kafka producers, Flink Table API applications, or other streaming tasks
- The relevant skill will automatically activate based on your request
Example prompts:
- "Create a Kafka producer that sends user profile update events to Confluent Cloud"
- "Build a Flink Table API application in Python to filter streaming data"
- "Set up a streaming pipeline with Schema Registry"
This repository includes the following skills:
| Skill | Description |
|---|---|
| kafka-schema-registry | Scan a project or repository to identify Kafka applications, extract schemas from data models, tag PII fields, generate Terraform for Confluent Schema Registry registration, and produce a migration report with rollout ordering. Automates the migration path from unmanaged schemas to Schema Registry with proper governance and compliance. |
| kafka-streams-programming | Architect, build, and debug Kafka Streams applications that run as a library inside your JVM with no separate cluster required. Handles topology design, pattern selection (joins, windows, aggregations), code generation for complete projects with proper Schema Registry integration, and troubleshooting production issues like rebalancing loops, state store problems, and performance tuning. |
| developing-kafka-python-client | Scaffold a Python Kafka producer/consumer project using confluent-kafka-python with Schema Registry serialization (Avro, JSON Schema, or Protobuf). Supports async (AIOProducer) and synchronous (Producer) modes, Confluent Cloud, and local Docker. |
| confluent-cloud-cdc-tableflow | 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 SQL Server, MySQL, PostgreSQL, Oracle, and DynamoDB to Iceberg or Delta Lake tables. |
agent-skills/
├── skills/ # Individual skill definitions
│ ├── skill-name/ # Individual skill directory
│ │ ├── SKILL.md # Skill description
│ │ ├── evals/ # Evaluation tests for the skill
│ │ └── references/ # Assets referenced by SKILL.md
│ └── ... # Additional skills
├── README.md # This file
├── .claude-plugin/ # Claude marketplace and plugin definition
└── .cursor-plugin/ # Cursor plugin definition
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.