README
AI Agent Skills by Confluent
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.
Installation
Claude
/plugin marketplace add confluentinc/agent-skills
/plugin install streaming-skills-plugin@confluent-agent-skills
skills CLI
This lets you pick specific skills to install and supports most agents.
npx skills add confluentinc/agent-skills
Note on IBM Bob
If 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.
Quick start
- 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"
Contributing
We welcome external contributions! Read CONTRIBUTING.md for more, and please note that the confluent-skill-creator and confluent-skill-reviewer skills are available to help with development.
Skills
This repository includes the following skills:
| Skill | Description |
|---|---|
| flink-udf | Build and deploy custom Apache Flink user-defined functions (UDFs) in Java to extend Flink SQL and Table API capabilities with custom logic. Supports scalar UDFs for value transformations, user-defined table functions (UDTFs) for one-to-many operations, and process table functions (PTFs) for advanced stateful stream processing. |
| 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-java-client | Scaffold a Java Kafka producer/consumer project using the Apache Kafka Java clients (KafkaProducer/KafkaConsumer) with Confluent Schema Registry serialization (Avro by default, plus JSON Schema and Protobuf). Supports async callback and synchronous send patterns, Maven or Gradle, and Confluent Cloud, local Docker, or WarpStream. |
| 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. |
| msk-migration | Assess and plan a migration from AWS MSK (Managed Streaming for Apache Kafka) to Confluent Cloud. Covers KCP-based environment scanning, cluster sizing, and switchover planning (Cluster Linking vs Gateway/Zero-Cut); directs users to Confluent docs/account team for execution and post-cutover validation. |
| confluent-skill-reviewer | Audit a skill in this repo against the Agent Skills spec (agentskills.io), the Confluent conventions in CLAUDE.md, and the PR template gates. Wraps skill-validator opportunistically and adds Confluent-specific checks: lazy-loaded references, trigger-overlap anti-clauses, evals-as-contract, 90% eval threshold, SME + DTX/DevRel reviewer assignment. Runs in PR-diff, single-skill, or repo-wide mode. |
| confluent-skill-creator | Create new Confluent-specific skills that are tested against real Confluent environments (Cloud, Platform, Apache Kafka, or WarpStream). Guides scope and platform targeting, intent capture, spec-compliant authoring, credential setup (.env or YAML), end-to-end testing with bundled scripts, eval-driven iteration, and packaging. For building new skills — not for using existing product skills. |
Repository layout
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
License
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.
Contributors
Similar repos
Languages
Python
55.7%
Java
23.5%
Shell
12.3%
HTML
3.1%
Makefile
2.7%
C#
1.1%
README
AI Agent Skills by Confluent
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.
Installation
Claude
/plugin marketplace add confluentinc/agent-skills
/plugin install streaming-skills-plugin@confluent-agent-skills
skills CLI
This lets you pick specific skills to install and supports most agents.
npx skills add confluentinc/agent-skills
Note on IBM Bob
If 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.
Quick start
- 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"
Contributing
We welcome external contributions! Read CONTRIBUTING.md for more, and please note that the confluent-skill-creator and confluent-skill-reviewer skills are available to help with development.
Skills
This repository includes the following skills:
| Skill | Description |
|---|---|
| flink-udf | Build and deploy custom Apache Flink user-defined functions (UDFs) in Java to extend Flink SQL and Table API capabilities with custom logic. Supports scalar UDFs for value transformations, user-defined table functions (UDTFs) for one-to-many operations, and process table functions (PTFs) for advanced stateful stream processing. |
| 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-java-client | Scaffold a Java Kafka producer/consumer project using the Apache Kafka Java clients (KafkaProducer/KafkaConsumer) with Confluent Schema Registry serialization (Avro by default, plus JSON Schema and Protobuf). Supports async callback and synchronous send patterns, Maven or Gradle, and Confluent Cloud, local Docker, or WarpStream. |
| 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. |
| msk-migration | Assess and plan a migration from AWS MSK (Managed Streaming for Apache Kafka) to Confluent Cloud. Covers KCP-based environment scanning, cluster sizing, and switchover planning (Cluster Linking vs Gateway/Zero-Cut); directs users to Confluent docs/account team for execution and post-cutover validation. |
| confluent-skill-reviewer | Audit a skill in this repo against the Agent Skills spec (agentskills.io), the Confluent conventions in CLAUDE.md, and the PR template gates. Wraps skill-validator opportunistically and adds Confluent-specific checks: lazy-loaded references, trigger-overlap anti-clauses, evals-as-contract, 90% eval threshold, SME + DTX/DevRel reviewer assignment. Runs in PR-diff, single-skill, or repo-wide mode. |
| confluent-skill-creator | Create new Confluent-specific skills that are tested against real Confluent environments (Cloud, Platform, Apache Kafka, or WarpStream). Guides scope and platform targeting, intent capture, spec-compliant authoring, credential setup (.env or YAML), end-to-end testing with bundled scripts, eval-driven iteration, and packaging. For building new skills — not for using existing product skills. |
Repository layout
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
License
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.
Similar repos
Contributors
Languages
Python
55.7%
Java
23.5%
Shell
12.3%
HTML
3.1%
Makefile
2.7%
C#
1.1%