How do I install the Flink Udf skill?

Run npx skillmds@latest add confluentinc/flink-udf in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Flink Udf safe to use?

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Which AI agents work with Flink Udf?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Flink Udf free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Flink Udf?

confluentinc (@confluentinc) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Flink Udf by confluentinc — Agent Skill

Name: Flink Udf
Author: confluentinc

Flink Udf

Build and deploy Apache Flink user-defined functions (UDFs) in Java for stream processing over Kafka. Use this skill when users want to create scalar UDFs, user-defined table functions (UDTFs), or process table functions (PTFs) in Java, deploy them to Confluent Cloud or local Docker environments, and invoke them from Flink SQL or the Table API. Trigger on: Flink UDF, custom Flink function, process table function, PTF, UDTF, Flink user defined, extend Flink SQL, stateful stream processing with Flink. Do NOT trigger for: Kafka Streams UDFs (use kafka-streams-programming skill), general Flink job development without custom functions, CDC streaming data piplines that include Flink (prefer the confluent-cloud-cdc-tableflow skill), Flink connector setup, or Kafka producer/consumer code.

confluentinc 592f7d1 10 files · 70.3 KB Updated Sep 17, 2026

File contents

Flink User-Defined Functions (UDFs)

Build and deploy custom functions in Java for Apache Flink to extend SQL and Table API capabilities with custom logic.

Function Types

Before proceeding, identify which type of function the user needs:

Scalar UDF: Maps input values to a single output value (e.g., custom hash, string manipulation, calculations)
User-Defined Table Function (UDTF): Maps input to multiple output rows (e.g., split strings, explode arrays)
Process Table Function (PTF): Advanced stateful processing with N-to-M semantics, managed state, and timers (e.g., windowing, deduplication, state machines)

Gather Requirements

Ask the user these questions to determine the implementation path (if not already clear from context):

Deployment target: Confluent Cloud or local Docker?
Infrastructure: Deploy new infrastructure (Kafka + Flink) or use existing?
Invocation method: Flink SQL or Table API?

Route to Implementation Guide

Based on the answers above, read the appropriate reference file:

Confluent Cloud Deployment

Scalar UDF or UDTF → Read references/udf-udtf-java-confluent-cloud.md
Process Table Function (PTF) → Read references/ptf-java-confluent-cloud.md

If infrastructure setup is needed, also read references/confluent-cloud-setup.md first.

Local Docker Deployment

Scalar UDF or UDTF → Read references/udf-udtf-java-local.md
Process Table Function (PTF) → Read references/ptf-java-local.md

If infrastructure setup is needed, also read references/local-docker-setup.md first.

Implementation Workflow

After reading the appropriate reference:

Set up infrastructure (if needed)
Generate boilerplate code for the function
Implement the business logic
Build and package the JAR
Confirm the deployment plan with the user. Before any resource-modifying call (confluent flink artifact create, docker cp into a running container, CREATE FUNCTION, etc.), present the plan and wait for explicit approval. Show:
- Artifact name and JAR path
- Function name to register
- Target environment (Confluent Cloud env + compute pool ID, or local Docker container name)
- The exact commands and SQL that will run Do not proceed to steps 6–7 until the user confirms.
Deploy the artifact
Register the function in Flink
Test the function with sample data
Provide usage examples (SQL or Table API)

Keep code scaffolding concise and focused on the user's specific requirements. Avoid over-engineering.

---name: flink-udfdescription: Build and deploy Apache Flink user-defined functions (UDFs) in Java for stream processing over Kafka. Use this skill when users want to create scalar UDFs, user-defined table functions (UDTFs), or process table functions (PTFs) in Java, deploy them to Confluent Cloud or local Docker environments, and invoke them from Flink SQL or the Table API. Trigger on: Flink UDF, custom Flink function, process table function, PTF, UDTF, Flink user defined, extend Flink SQL, stateful stream processing with Flink. Do NOT trigger for: Kafka Streams UDFs (use kafka-streams-programming skill), general Flink job development without custom functions, CDC streaming data piplines that include Flink (prefer the confluent-cloud-cdc-tableflow skill), Flink connector setup, or Kafka producer/consumer code.---# Flink User-Defined Functions (UDFs)Build and deploy custom functions in Java for Apache Flink to extend SQL and Table API capabilities with custom logic.## Function TypesBefore proceeding, identify which type of function the user needs:- **Scalar UDF**: Maps input values to a single output value (e.g., custom hash, string manipulation, calculations)- **User-Defined Table Function (UDTF)**: Maps input to multiple output rows (e.g., split strings, explode arrays)- **Process Table Function (PTF)**: Advanced stateful processing with N-to-M semantics, managed state, and timers (e.g., windowing, deduplication, state machines)## Gather RequirementsAsk the user these questions to determine the implementation path (if not already clear from context):1. **Deployment target**: Confluent Cloud or local Docker?2. **Infrastructure**: Deploy new infrastructure (Kafka + Flink) or use existing?3. **Invocation method**: Flink SQL or Table API?## Route to Implementation GuideBased on the answers above, read the appropriate reference file:### Confluent Cloud Deployment- **Scalar UDF or UDTF** → Read `references/udf-udtf-java-confluent-cloud.md`- **Process Table Function (PTF)** → Read `references/ptf-java-confluent-cloud.md`If infrastructure setup is needed, also read `references/confluent-cloud-setup.md` first.### Local Docker Deployment- **Scalar UDF or UDTF** → Read `references/udf-udtf-java-local.md`- **Process Table Function (PTF)** → Read `references/ptf-java-local.md`If infrastructure setup is needed, also read `references/local-docker-setup.md` first.## Implementation WorkflowAfter reading the appropriate reference:1. **Set up infrastructure** (if needed)2. **Generate boilerplate code** for the function3. **Implement the business logic**4. **Build and package** the JAR5. **Confirm the deployment plan with the user.** Before any resource-modifying call (`confluent flink artifact create`, `docker cp` into a running container, `CREATE FUNCTION`, etc.), present the plan and wait for explicit approval. Show: - Artifact name and JAR path - Function name to register - Target environment (Confluent Cloud env + compute pool ID, or local Docker container name) - The exact commands and SQL that will run Do not proceed to steps 6–7 until the user confirms.6. **Deploy the artifact**7. **Register the function** in Flink8. **Test the function** with sample data9. **Provide usage examples** (SQL or Table API)Keep code scaffolding concise and focused on the user's specific requirements. Avoid over-engineering.

Frequently asked questions