Apache Kafka Stream Transformer

SkillCommunication

This skill lets your AI read and transform live event streams from Apache Kafka. Incoming messages are reshaped to match schemas you define, and any that fail are set aside in a holding queue instead of being lost. Your AI keeps a reliable place in the stream even when the work is shifted between consumers.

Available today. Use it from your connected AI after setup.

No other account needed.

Add the skill, then tell your AI which event stream to work with and how its messages should be transformed.

Then ask your AI: use the Apache Kafka Stream Transformer skill

What your AI can do with it

Process event streams in real time as messages arrive
Transform messages to match schemas you configure
Keep processing steady when stream work is redistributed across consumer groups
Record progress through the stream so processing resumes where it left off
Send messages that fail transformation to a dead-letter queue so they are set aside, not lost

What this skill tells your AI

The instructions your AI receives, as published by agentskillexchange/skills in skills/apache-kafka-stream-transformer-2/SKILL.md and read by ahel’s review.

Processes real-time event streams using KafkaJS consumer groups and transforms messages with configurable schemas. Handles partition rebalancing, offset commits, and dead-letter queue routing for failed transformations.

Installation

Use the upstream install or setup path that matches your environment:

docker run -p 9092:9092 apache/kafka:latest

Requirements and caveats from upstream:

Using docker image:
See docker/README.md for detailed information.

Basic usage or getting-started notes:

Build a JAR and run it
Run unit/integration tests
./gradlew test -Pkafka.test.run.flaky=true # runs tests that are marked as flaky
Source: https://github.com/apache/kafka
Extracted from upstream docs: https://raw.githubusercontent.com/apache/kafka/HEAD/README.md