Flink Udf

Organization

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.

Overview

Publisher	confluentinc
Repository	`agent-skills`
Skill name	`flink-udf`
Stars	60
Forks	11
Bundled files	9
License	Apache-2.0
Links	Skill source Repository

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
9 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by confluentinc on GitHub. Read the source before you install it.

Installation

Install the Flink Udf AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/confluentinc/agent-skills/tree/main/skills/flink-udf
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/confluentinc/agent-skills.git /tmp/agent-skills
mkdir -p .claude/skills
cp -r /tmp/agent-skills/skills/flink-udf .claude/skills/flink-udf

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Flink Udf in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Flink Udf on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Flink Udf is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Flink Udf AI skill do?

Why use Flink Udf on TypingMind?

Because you install it once and use it with any model. Flink Udf is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Flink Udf in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/confluentinc/agent-skills/tree/main/skills/flink-udf. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Flink Udf?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Flink Udf?

As many as you like. As long as a model supports skills, you can use Flink Udf with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Flink Udf AI skill free?

Yes. It is published on GitHub by confluentinc under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Flink User-Defined Functions (UDFs)

Function Types

Gather Requirements

Route to Implementation Guide

Confluent Cloud Deployment

Local Docker Deployment

Implementation Workflow

Bundled files

Frequently asked questions

Related AI Skills

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