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Kafka Resilience And Schema Evolution

Community
vaquarkhan
kafka-resilience-and-schema-evolution

Enforces production Kafka guardrails including non-breaking schema evolution, dead-letter queues for poison messages, and acks=all producer durability. Use when designing or changing Kafka topics, producers, consumers, schema registry policies, or streaming recovery paths.

Overview

Publishervaquarkhan
Repositorydata-engineering-agent-skills
Skill namekafka-resilience-and-schema-evolution
Stars
60
Forks
8
Bundled files
Instructions only
LicenseMIT
Links
Skill sourceRepositoryHomepage
  • 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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by vaquarkhan on GitHub. Read the source before you install it.

Installation

Install the Kafka Resilience And Schema Evolution AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

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.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/vaquarkhan/data-engineering-agent-skills/tree/main/skills/kafka-resilience-and-schema-evolution
  4. 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.
2

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/vaquarkhan/data-engineering-agent-skills.git /tmp/data-engineering-agent-skills
mkdir -p .claude/skills
cp -r /tmp/data-engineering-agent-skills/skills/kafka-resilience-and-schema-evolution .claude/skills/kafka-resilience-and-schema-evolution
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Kafka Resilience And Schema Evolution 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 Kafka Resilience And Schema Evolution 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 Kafka Resilience And Schema Evolution 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.

Kafka Resilience And Schema Evolution

Overview

Generic streaming guidance is not enough for production Kafka. Agents routinely introduce breaking schema changes, under-provisioned durability settings, and missing poison-message isolation. This skill mandates enforceable broker, producer, consumer, and registry guardrails before any production change ships.

When to Use

  • creating or modifying Kafka topics, producers, or consumers
  • setting or changing schema registry compatibility policies
  • designing dead-letter queue (DLQ) routing for poison pill messages
  • hardening producer durability (acks, retries, idempotence)
  • reviewing consumer lag, replay, or failover behavior on Kafka-backed pipelines

Pair with streaming-and-messaging-systems for broader event design. Pair with avro-protobuf-json-schema-registry when registry subjects and compatibility CI are in scope.

Workflow

  1. Define the production contract before broker changes. Document:

    • topic key strategy and partition count rationale
    • retention, compaction, and replay policy
    • schema format and registry subject naming
    • consumer groups and downstream sinks
    • delivery semantics target (at-least-once with idempotent sinks, or stricter)

  2. Enforce producer durability defaults. Require unless explicitly waived with owner approval:

    • acks=all (or acks=-1)
    • enable.idempotence=true when ordering and deduplication matter
    • bounded retries with delivery.timeout.ms aligned to SLA
    • max.in.flight.requests.per.connection=1 when strict ordering is required
    • TLS/SASL configuration documented for non-development clusters

  3. Block breaking schema evolution. Before any schema change:

    • set compatibility policy per subject (BACKWARD, FORWARD, or FULL — not NONE in production)
    • run compatibility checks in CI against registered schemas
    • document producer-then-consumer or consumer-then-producer rollout order
    • reject field removals, renames, or type changes without migration plan
    • load references/kafka-production-guardrails.md for DLQ and evolution patterns

  4. Mandate dead-letter and poison pill isolation. Every production consumer that parses external payloads must define:

    • DLQ topic or sink with retention and access controls
    • classification rules (deserialization failure, schema mismatch, business rule violation)
    • alert routing when DLQ rate exceeds threshold
    • replay procedure with deduplication keys
    • no silent drop of unparseable records

  5. Make lag and recovery observable. Plan for:

    • consumer group lag alerts with owner routing
    • offset reset policy documented and restricted
    • replay runbook that does not bypass DLQ classification
    • broker disk and retention monitoring for high-throughput topics

  6. Load MCP observability when diagnosing live lag. Use mcp-data-observability-integration with mcp/kafka.mcp.json to inspect consumer group lag and topic metadata before changing consumer code or partition counts.

Common Rationalizations

RationalizationReality
"acks=1 is fine because Kafka is durable."Leader acknowledgment without full ISR acknowledgment loses events under failure scenarios.
"We can fix schema breaks by redeploying consumers quickly."Breaking changes propagate to many consumers and batch sinks before redeploy completes.
"DLQs add too much operational overhead."Poison pills without DLQs stall partitions, inflate lag, and hide data loss as consumer retries.
"Schema compatibility NONE is okay for internal topics."Internal topics still feed warehouses, stream processors, and audit systems.

Red Flags

  • production subjects use NONE compatibility
  • producers use acks=0 or acks=1 without documented waiver
  • consumers have no DLQ path for deserialization failures
  • schema changes ship without CI compatibility validation
  • consumer group lag has no alert owner
  • replay procedures reset offsets without reconciliation or publish pause

Verification

  • Producer durability settings meet acks=all and idempotence requirements
  • Schema compatibility policy is set and CI-validated for production subjects
  • DLQ routing, alerts, and replay procedure are documented
  • Consumer lag and retention monitoring exist with named owners
  • Rollout order for schema changes is explicit and tested in non-production

Frequently asked questions

What does the Kafka Resilience And Schema Evolution AI skill do?

Enforces production Kafka guardrails including non-breaking schema evolution, dead-letter queues for poison messages, and acks=all producer durability. Use when designing or changing Kafka topics, producers, consumers, schema registry policies, or streaming recovery paths.

Why use Kafka Resilience And Schema Evolution on TypingMind?

Because you install it once and use it with any model. Kafka Resilience And Schema Evolution 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 Kafka Resilience And Schema Evolution in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/vaquarkhan/data-engineering-agent-skills/tree/main/skills/kafka-resilience-and-schema-evolution. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Kafka Resilience And Schema Evolution?

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 Kafka Resilience And Schema Evolution?

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

Is the Kafka Resilience And Schema Evolution AI skill free?

Yes. It is published on GitHub by vaquarkhan under the MIT 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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