Kafka Connector Review logo

Kafka Connector Review

Organization
lensesio
kafka-connector-review

Review Kafka Connect connector configurations for common misconfigurations using the Lenses MCP server. Checks error handling, DLQ setup, converters, transforms, task count and task health. Use when user says "review connectors", "check connector configs", "why is my connector failing" or asks about Kafka Connect configuration. Do NOT use for creating, deploying or controlling connectors.

Overview

Publisherlensesio
Repositoryagentic-engineering-for-apache-kafka
Skill namekafka-connector-review
Stars
67
Forks
19
Bundled files
1
LicenseMIT
Links
  • 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.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Kafka Connector Review 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/lensesio/agentic-engineering-for-apache-kafka/tree/main/skills/kafka-connector-review
  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/lensesio/agentic-engineering-for-apache-kafka.git /tmp/agentic-engineering-for-apache-kafka
mkdir -p .claude/skills
cp -r /tmp/agentic-engineering-for-apache-kafka/skills/kafka-connector-review .claude/skills/kafka-connector-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Kafka Connector Review 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 Connector Review 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 Connector Review 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 Connect Configuration Review

Reviews Kafka Connect connector configurations for common misconfigurations. Connectors are defined as JSON/YAML and are entirely language-agnostic.

Target environment: $ARGUMENTS

Workflow

Copy this checklist and track your progress:

Connector Review Progress:
- [ ] Step 1: List all connectors
- [ ] Step 2: Inspect each connector's configuration
- [ ] Step 3: Validate configurations against plugin schemas
- [ ] Step 4: Audit for common misconfigurations
- [ ] Step 5: Generate report
  1. List all connectors with status and task states
  2. Inspect each connector's configuration in detail
  3. Validate configurations against plugin schemas
  4. Audit for common misconfigurations
  5. Report findings with current and recommended configs

Step 1: List All Connectors

Use the Lenses MCP list_kafka_connectors tool to get all connectors with:

  • Connector name, class and type (source/sink)
  • Status (RUNNING, PAUSED, FAILED, UNASSIGNED)
  • Task states and count
  • Cluster info

Flag immediately:

  • Critical: Connectors in FAILED state
  • Critical: Tasks in FAILED state
  • Warning: Connectors in PAUSED state with no obvious reason

Expected output: List of all connectors with name, class, status and task states.

Validation: If no connectors are returned, report that no Connect cluster is configured and stop.

Step 2: Inspect Configurations

Use the Lenses MCP get_kafka_connector_target_definition tool to get the full configuration YAML for each connector.

Step 3: Validate Configurations

Use the Lenses MCP validate_connector_configuration tool to validate each connector's config against its plugin's schema. This catches:

  • Missing required configuration fields
  • Invalid configuration values
  • Type mismatches

Step 4: Audit Common Misconfigurations

Error Handling

  • Critical: errors.tolerance=all without errors.deadletterqueue.topic.name (silently drops messages)
  • Warning: Missing errors.tolerance (defaults to none, connector stops on any error)
  • Warning: Missing errors.log.enable=true (errors not logged)
  • Suggestion: Add errors.deadletterqueue.context.headers.enable=true for richer DLQ metadata

Converters

  • Warning: key.converter or value.converter mismatch with topic serialisation format
  • Warning: Using org.apache.kafka.connect.storage.StringConverter for structured data (should use Avro/JSON/Protobuf converter)
  • Warning: Missing schemas.enable setting where schemas are expected

Transforms (SMTs)

  • Warning: Complex transform chains (> 3 SMTs) that may be hard to debug
  • Suggestion: Consider Kafka Streams or ksqlDB for complex transformations
  • Verify transform class names are valid and in the correct order

Task Count

  • Warning: tasks.max=1 on high-throughput connectors (limits parallelism)
  • Suggestion: Source connectors - tasks.max should align with source partitioning
  • Suggestion: Sink connectors - tasks.max should align with topic partition count

Connection and Retry

  • Warning: Missing retry configuration for connectors that interact with external systems
  • Warning: Missing connection timeout settings
  • Suggestion: Set explicit consumer.override.* or producer.override.* for performance tuning

Naming Conventions

  • Suggestion: Connector names should follow a consistent pattern (e.g., {source/sink}-{system}-{entity})

Success Criteria

Quantitative

  • Triggers on 90% of connector-related queries (test with 10-20 varied phrasings)
  • Completes review in under 10 MCP tool calls
  • Catches 100% of validation errors reported by the plugin schema

Qualitative

  • Failed connectors are flagged immediately in the status overview
  • Error handling gaps (missing DLQ, silent drops) are always identified
  • Report is useful for both connector operators and developers

Examples

Example 1: Routine connector review

User says: "Review all connectors in staging"

Actions:

  1. List all connectors with status
  2. Inspect and validate each connector's configuration
  3. Audit for common misconfigurations Result: Full report covering all connectors with prioritised findings

Example 2: Investigating a failed connector

User says: "My sink connector is failing, can you check why?"

Actions:

  1. List connectors and identify those in FAILED state
  2. Get the full configuration for the failed connector
  3. Validate config against plugin schema
  4. Check for common issues (DLQ, converters, error handling) Result: Diagnosis of the specific failure with remediation steps

Example 3: Single connector deep dive

User says: "Review the config for the elasticsearch-sink connector"

Actions:

  1. Fetch the target definition for the named connector
  2. Validate against its plugin schema
  3. Audit error handling, converters, task count Result: Focused report on a single connector

Troubleshooting

No connectors returned

Cause: No Kafka Connect cluster is configured in the environment or no connectors are deployed. Solution: Verify that a Connect cluster exists via Lenses UI. Check get_deployment_targets for available Connect clusters.

Validation fails with unknown plugin

Cause: The connector plugin class is not installed on the Connect cluster. Solution: Report the missing plugin. This is a deployment issue, not a configuration issue.

Connector shows RUNNING but tasks are FAILED

Cause: The connector framework is running but individual tasks have encountered errors. Solution: Check each task's status. Common causes include authentication failures, network issues or schema mismatches with the external system.

Output Format

## Connector Review Report

### Environment: {name}

### Connector Status Overview
| Connector | Class | Status | Tasks | Failed Tasks |
|-----------|-------|--------|-------|-------------|
| name | class | RUNNING | 3/3 | 0 |

### Critical (must fix)
- [connector-name] Description of the issue
  Current: {current config} | Recommended: {recommended config}

### Warning (should fix)
- [connector-name] Description of the issue
  Current: {current config} | Recommended: {recommended config}

### Validation Errors
- [connector-name] {field}: {validation error message}

### Suggestion (consider improving)
- [connector-name] Description of the suggestion
  Recommendation: {how to improve}

### Summary
- X connectors reviewed
- Y critical issues found
- Z warnings found
- Failed connectors: N
- Failed tasks: M

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 Kafka Connector Review AI skill do?

Review Kafka Connect connector configurations for common misconfigurations using the Lenses MCP server. Checks error handling, DLQ setup, converters, transforms, task count and task health. Use when user says "review connectors", "check connector configs", "why is my connector failing" or asks about Kafka Connect configuration. Do NOT use for creating, deploying or controlling connectors.

Why use Kafka Connector Review on TypingMind?

Because you install it once and use it with any model. Kafka Connector Review 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 Connector Review in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/lensesio/agentic-engineering-for-apache-kafka/tree/main/skills/kafka-connector-review. 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 Kafka Connector Review?

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 Connector Review?

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

Is the Kafka Connector Review AI skill free?

Yes. It is published on GitHub by lensesio 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇