# kafka-development

A skill that hands your agent Kafka best practices: topics, partitions, producer and consumer settings, Schema Registry. Useful when you build event-driven code.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 1200 installs · ★ 286
- Author: Mindrally
- License: Apache-2.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/Mindrally/skills/tree/HEAD/kafka-development/SKILL.md
- Updated: 2026-10-08
- HTML: https://affpapa.org/en/nevibecode/skills/kafka-development

## What it does

The skill gives the agent a rulebook for Apache Kafka. It covers topic naming, partition count based on expected consumer parallelism, retention and replication factor. Then the message schema: Avro or JSON in Schema Registry, backward compatible from day one.

There is a step-by-step path from topic to production. For the producer the agent sets `acks=all`, idempotence and a partition key that spreads evenly. For the consumer — `enable.auto.commit=false`, with offsets committed only after successful processing. Separate steps handle observability (send-rate, consumer lag, under-replicated-partitions) and end-to-end tests with Testcontainers or an embedded broker.

An architecture section explains topics, partitions, brokers, consumer groups and offsets. Partition count can go up but never down, so check the math before you create the topic. Code samples cover a Java producer and a Java consumer.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add Mindrally/skills --skill kafka-development -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/kafka-development/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/kafka-development/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add Mindrally/skills --skill kafka-development -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/kafka-development/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add Mindrally/skills --skill kafka-development -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/kafka-development/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install Mindrally/skills kafka-development
```

2. **Manually: put the files in place**
- File: `.github/skills/kafka-development/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add Mindrally/skills --skill kafka-development -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/Mindrally/skills --path kafka-development
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/kafka-development/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add Mindrally/skills --skill kafka-development -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/Mindrally/skills/tree/HEAD/kafka-development/SKILL.md

## When to use it
- design a topic with partitions sized for consumer parallelism
- configure a producer with idempotence and retries
- write a consumer that commits offsets manually
- register a schema in Schema Registry with backward compatibility
- add lag metrics and dead-letter routing

## Example prompts
- help me design the orders topic: how many partitions and which key
- write a Java producer with acks=all and idempotence
- review my consumer: are offsets committed after processing?

## Source text · SKILL.md · Apache-2.0

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: a3604a06a23d7e72.

BEGIN THIRD-PARTY FILE a3604a06a23d7e72
```text
---
name: kafka-development
description: "Best practices for Apache Kafka event streaming and distributed messaging. Use when building event-driven architectures, implementing producer/consumer patterns, designing topic partitioning strategies, setting up Kafka Streams, configuring schema registries, or integrating change data capture pipelines."
metadata:
  maintainer: Mindrally
  source: https://github.com/Mindrally/skills
---

# Kafka Development

This skill provides best practices for Apache Kafka event streaming and distributed messaging systems. Apply these guidelines when building Kafka-based applications.

## Core Principles

- Kafka is a distributed event streaming platform for high-throughput, fault-tolerant messaging
- Unlike traditional pub/sub, Kafka uses a pull model - consumers pull messages from partitions
- Design for scalability, durability, and exactly-once semantics where needed
- Leave NO todos, placeholders, or missing pieces in the implementation

## Workflow: Setting Up a Kafka Producer-Consumer Pipeline

1. **Define the topic** — choose a descriptive name, set partition count based on expected consumer parallelism, and configure retention and replication factor.
2. **Design the message schema** — register an Avro or JSON schema in Schema Registry; ensure backward compatibility from the start.
3. **Implement the producer** — configure `acks=all`, enable idempotence, select a partition key that distributes evenly, and add error handling with retry logic.
4. **Implement the consumer** — set `enable.auto.commit=false`, pick an appropriate `auto.offset.reset` policy, process messages idempotently, and commit offsets only after successful processing.
5. **Add observability** — instrument producer send-rate, consumer lag, and broker under-replicated-partitions; propagate trace context in message headers.
6. **Test end-to-end** — use Testcontainers or an embedded Kafka broker to verify the full produce-consume-commit cycle, including failure and rebalance scenarios.
7. **Deploy and monitor** — roll out with lag alerts, dead-letter-topic routing for persistent failures, and dashboards for key broker and client metrics.

## Architecture Overview

### Core Components

- **Topics**: Categories/feeds for organizing messages
- **Partitions**: Ordered, immutable sequences within topics enabling parallelism
- **Producers**: Clients that publish messages to topics
- **Consumers**: Clients that read messages from topics
- **Consumer Groups**: Coordinate consumption across multiple consumers
- **Brokers**: Kafka servers that store data and serve clients

### Key Concepts

- Messages are appended to partitions in order
- Each message has an offset - a unique sequential ID within the partition
- Consumers maintain their own cursor (offset) and can read streams repeatedly
```
END THIRD-PARTY FILE a3604a06a23d7e72

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