Developing Kafka Python Client

Do NOT generate any code, scaffold any project, or modify any file until you have explicitly asked and received answers for questions #1 (existing app or greenfield), #2 (target environment), and #3 (producer, consumer, or both). If the user's prompt partially answers some questions, still confirm your understanding before generating. This applies to EVERY prompt regardless of how specific it appears.

Begin by announcing: "Using the Confluent Kafka Python Client skill to guide this project."

Confluent Kafka Python Client Creation

Generate a production-ready Python project for producing to and/or consuming from Kafka using confluent-kafka-python. Supports three target environments: Confluent Cloud (managed), Local Docker (open-source Kafka), and WarpStream (Kafka-compatible, object-storage-backed), and two producer styles: AsyncIO (non-blocking) and Synchronous (blocking). The generated code follows Confluent's best practices.

Step 1: Gather Requirements

Before generating any code, work through the questions below. Skip any question the user has already answered explicitly in their prompt — do not re-ask just for form's sake. For example, "build a producer and consumer on Confluent Cloud with an async producer" already answers #2, #3, and #4; only #1, #5, #6, #7, and #8 remain.

Mandatory confirmation gate — do not skip, even if the user answered every question. Before writing any file, you MUST send one message that:

1. Recaps the answers you extracted as a short bulleted list (e.g., "Target: Confluent Cloud · Components: producer + consumer · Producer style: async · From scratch: yes").
2. Asks any remaining open questions inline.
3. Explicitly asks the user to confirm or correct before you proceed.

Then STOP and wait for the user's reply. Do not generate files in the same turn as the recap, and do not proceed on the assumption that a fully-specified prompt implies consent to generate immediately — the recap catches misinterpretations of the prompt and is required even when questions #1–#8 are all pre-answered. The only way to skip the gate is if the user has already confirmed the recap earlier in this conversation.

Do not assume defaults for #1, #2, or #3 — if any of these are not answered by the prompt, you must ask.

1. Are you adding Kafka to an existing application, or starting from scratch?

• If the user has existing Python code (mentions an existing project, has a main.py, uses Flask/FastAPI/Django, etc.), do not scaffold a new project. Instead: (a) identify their existing producer or data-sending code, (b) ask whether they already have schemas registered in Schema Registry, (c) add Schema Registry integration to their existing code following the patterns in the reference files. Generate only the files they are missing (e.g., common.py, schemas/value.schema.json) and modify their existing code inline.
• If the user already produces to Kafka without Schema Registry (schemaless), help them migrate: (1) generate a JSON Schema from their existing message structure, (2) register it, and (3) replace their raw producer.produce() calls with serializer-backed calls. Do not discard their existing code.
• If starting from scratch, proceed with the full scaffold below.

1. Target environment? — Confluent Cloud, local Kafka (Docker), or WarpStream. Always prompt for this, even if the user didn't mention it. If they mention "open source", "local", "docker", "self-hosted", or just want to try Kafka without a cloud account, choose local Docker. If they mention "Confluent Cloud", "CC", or have existing cloud credentials, choose Confluent Cloud. If they mention "WarpStream", choose WarpStream. Default to Confluent Cloud if they confirm they don't have a preference, but always ask first.

• If WarpStream: Read references/warpstream-optimization.md and apply the librdkafka overrides from that reference. Key changes: disable idempotence, dramatically increase batch sizes and in-flight requests, set large fetch sizes, add ws_az= to client.id for zone-aware routing. Prefer null message keys for sticky partitioning unless entity-based ordering is required.

1. Producer, consumer, or both?
2. Async or synchronous producer? (Only if producer is requested.) Help the user choose:

• AsyncIO Producer (AIOProducer): Use when code runs under an event loop — FastAPI/Starlette, aiohttp, Sanic, asyncio workers — and must not block.
• Synchronous Producer (Producer): Use for scripts, batch jobs, and highest-throughput pipelines where the user controls threads/processes and can call poll()/flush() directly.

If the user mentions an async framework (FastAPI, aiohttp, Sanic) or uses asyncio, default to AsyncIO. If they mention scripts, batch, ETL, or don't have a preference, default to Synchronous.

1. Do you have an existing schema you'd like to use? If yes, ask the user to paste it or provide the file path, then use it as the schemas/value.schema.json instead of generating one. If no, proceed to ask about their data fields.
2. What kind of data are you producing? (Only if the user doesn't have an existing schema. Get field names and types so you can generate a matching JSON Schema and sample data.)
3. Topic name? (Default: demo-topic)
4. Consumer group ID? (Only if consumer; default: python-consumer-group)

Don't ask about Schema Registry — always include it. For Confluent Cloud and local Docker, always use JSON Schema. If the target is WarpStream, ask which Schema Registry implementation they are using — WarpStream's built-in schema registry only supports Avro and Protobuf (GET /schemas/types returns ["AVRO","PROTOBUF"]), so if they are using it, ask whether they prefer Avro or Protobuf (default to Avro). If they are using a different SR (e.g., Confluent Cloud Schema Registry), JSON Schema is fine.

Common Agent Mistakes

| Thought | Reality | |---------|---------| | "The user mentioned FastAPI, so I know it's async — skip the questions" | Still confirm. They might want a sync background worker alongside FastAPI. | | "I'll use Avro since it's more widely used" | This skill uses JSON Schema by default. Exception: WarpStream's built-in schema registry only supports Avro and Protobuf — if the user is using WarpStream SR, use Avro by default. If they're using a different SR (e.g., Confluent Cloud SR), JSON Schema is fine regardless of the Kafka environment. | | "I'll skip Schema Registry to keep it simple" | Schema Registry is non-negotiable. Every project includes it. | | "I'll use auto.register.schemas=True for convenience" | Always False. Explicit registration is a core principle. | | "I'll create a producer in produce() — it's cleaner" | One producer instance, created in main(), passed as a parameter. Always. | | "The user wants sync, so the consumer should be sync too" | Consumer is always async (AIOConsumer). This is a deliberate design decision. | | "I'll add headers= to the AIOProducer for schema ID" | AIOProducer.produce() raises NotImplementedError on headers. Only sync producers use headers. | | "I'll swap AsyncJSONSerializer for AsyncAvroSerializer and keep the call site the same" | JSONSerializer takes schema_str first; AvroSerializer takes schema_registry_client first. Calling positionally across formats raises TypeError: ... got multiple values for argument 'schema_registry_client'. Always pass both as kwargs. | | "I'll set message.max.bytes=64000000 on the producer config and fetch.max.bytes=50242880 on the consumer — they're independent" | Not in librdkafka. message.max.bytes is a client-global config (unlike Java's per-role max.request.size), so when get_kafka_config() is shared between producer and consumer, the consumer inherits it. librdkafka enforces fetch.max.bytes >= message.max.bytes at consumer construction and raises KafkaError{_INVALID_ARG, ... "fetch.max.bytes must be >= message.max.bytes"}. Use the WarpStream librdkafka consumer values in references/warpstream-optimization.md (fetch.max.bytes=67108864) — they're sized to satisfy this constraint. |

Step 1b: Confirm Understanding

After gathering all answers, present a confirmation summary before generating any code:

Before I generate the project, let me confirm:
- Project type: [Greenfield scaffold / Migration of existing code]
- Environment: [Confluent Cloud (SASL_SSL) / Local Docker (PLAINTEXT) / WarpStream]
- Schema format: [JSON Schema / Avro / Protobuf] (Avro or Protobuf if using WarpStream's built-in SR)
- Components: [Producer only / Consumer only / Both]
- Producer style: [AsyncIO (AIOProducer) / Synchronous (Producer)] (if applicable)
- Schema: [brief description of user's data fields]
- Topic: [topic name]
- Consumer group: [group ID] (if consumer)

Does this look right?

Wait for user confirmation before proceeding to Step 2. If the user corrects anything, update your understanding and re-confirm.

Step 2: Generate the Project

Decision Flowchart

digraph decisions {
  "Q1: Existing app?" -> "Migration path:\nmodify existing code" [label="yes"];
  "Q1: Existing app?" -> "Q2: Environment?" [label="no / greenfield"];
  "Q2: Environment?" -> "Cloud config\n(SASL_SSL)" [label="Confluent Cloud"];
  "Q2: Environment?" -> "Local Docker config\n(PLAINTEXT) + docker-compose.yml" [label="local / docker / OSS"];
  "Q2: Environment?" -> "WarpStream config\n(apply overrides from\nreferences/warpstream-optimization.md)" [label="WarpStream"];
  "Cloud config\n(SASL_SSL)" -> "Q3: Components?";
  "Local Docker config\n(PLAINTEXT) + docker-compose.yml" -> "Q3: Components?";
  "WarpStream config\n(apply overrides from\nreferences/warpstream-optimization.md)" -> "Which SR?\n(WarpStream SR → Avro/Protobuf;\nother SR → JSON Schema)";
  "Which SR?\n(WarpStream SR → Avro/Protobuf;\nother SR → JSON Schema)" -> "Q3: Components?";
  "Q3: Components?" -> "Q4: Async or sync?" [label="producer requested"];
  "Q3: Components?" -> "Generate consumer\n(always async AIOConsumer)" [label="consumer only"];
  "Q4: Async or sync?" -> "AIOProducer path\nAsyncJSONSerializer\n(no headers support)" [label="async / event-loop"];
  "Q4: Async or sync?" -> "Producer path\nJSONSerializer\n(header-based schema ID)" [label="sync / batch / ETL"];
}

Create this file structure in the user's chosen directory:

/
├── producer.py          # (if requested)
├── consumer.py          # (if requested)
├── common.py            # shared config loading + verification helpers
├── schemas/
│   └── value.schema.json # JSON Schema (or value.avsc / value.proto when using WarpStream SR)
├── tests/
│   └── test_project.py  # unit tests (always generated)
├── .env.example         # template for credentials
├── requirements.txt
├── docker-compose.yml   # (local Docker path only)

Security

NEVER read, open, or display .env files. They contain API keys and secrets. Only generate .env.example with placeholder values. If the user asks you to debug a connection issue, ask them to verify their .env values themselves — do not read the file.

Core Principles

These principles matter because they prevent the most common production issues with Kafka Python clients:

1. Reuse the producer instance. Creating a new producer per message is expensive — each one opens new TCP connections, does SASL handshakes, and fetches metadata. Create one producer and reuse it for all messages. The produce function should accept the producer as a parameter, not instantiate one.

1. Always use Schema Registry with JSON Schema. Schema Registry enforces a contract between producers and consumers. Without it, schema changes silently break downstream consumers. This skill uses JSON Schema by default. Schema Registry supports Avro, Protobuf, and JSON Schema — JSON Schema is chosen because: (1) Python has first-class JSON support with no code generation step, (2) confluent-kafka-python provides JSONSerializer/JSONDeserializer out of the box, (3) it is the most approachable format for Python developers already working with JSON/dict data.

WarpStream Schema Registry exception: WarpStream's built-in schema registry only supports Avro and Protobuf — its GET /schemas/types endpoint returns ["AVRO","PROTOBUF"]. JSON Schema is not available when using WarpStream's SR. If the user is running WarpStream and using its built-in SR, use Avro by default (or Protobuf if the user prefers). Use AvroSerializer/AvroDeserializer from confluent_kafka.schema_registry.avro (or ProtobufSerializer/ProtobufDeserializer from confluent_kafka.schema_registry.protobuf). For async producers, use AsyncAvroSerializer from confluent_kafka.schema_registry._async.avro (or AsyncProtobufSerializer from confluent_kafka.schema_registry._async.protobuf). Generate an Avro schema file at schemas/value.avsc (or schemas/value.proto for Protobuf) instead of schemas/value.schema.json — use references/value.avsc as the starting point and adapt to the user's domain. When generating producer/consumer code on the Avro path, copy from references/producer_avro.py (async), references/producer_avro_sync.py (sync), and references/consumer_avro.py (async) — not the JSON reference files — because the constructor signatures differ (see the Avro constructor warning below). If the user is running WarpStream but pointing at a different SR (e.g., Confluent Cloud Schema Registry), JSON Schema works fine — use the default path.

Register schemas as a separate explicit step before creating the serializer. Use a dedicated register_schema() function that calls sr_client.register_schema() and lets errors (auth failures, network errors, permission denials) propagate immediately — never wrap registration in a bare try/except. Then configure the serializer with auto.register.schemas=False and use.latest.version=True. This ensures the serializer never silently auto-registers and aligns with production practice where CI/CD registers schemas, not application startup.

Use the appropriate serializer for the chosen producer style and schema format:

• JSON Schema (default): AsyncJSONSerializer / AsyncJSONDeserializer from confluent_kafka.schema_registry._async.json_schema for async, or JSONSerializer / JSONDeserializer from confluent_kafka.schema_registry.json_schema for synchronous.
• Avro (default when using WarpStream SR): AsyncAvroSerializer / AsyncAvroDeserializer from confluent_kafka.schema_registry._async.avro for async, or AvroSerializer / AvroDeserializer from confluent_kafka.schema_registry.avro for synchronous.
• Protobuf (alternative when using WarpStream SR): AsyncProtobufSerializer / AsyncProtobufDeserializer from confluent_kafka.schema_registry._async.protobuf for async, or ProtobufSerializer / ProtobufDeserializer from confluent_kafka.schema_registry.protobuf for synchronous.

1. Choose the right producer style. The confluent-kafka-python library offers two producer APIs:

• AsyncIO Producer (AIOProducer from confluent_kafka.aio): Non-blocking, integrates with asyncio event loops. Use with AsyncJSONSerializer from confluent_kafka.schema_registry._async.json_schema and AsyncSchemaRegistryClient. Best for applications already running an event loop (FastAPI, aiohttp, Sanic, asyncio workers).
• Synchronous Producer (Producer from confluent_kafka): Blocking calls with delivery callbacks. Use with JSONSerializer from confluent_kafka.schema_registry.json_schema and SchemaRegistryClient. Best for scripts, batch jobs, and highest-throughput pipelines where the user controls threads/processes and can call poll()/flush() directly.

Always ask the user which style fits their use case. The consumer always uses AIOConsumer (async) — long-ru

…

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: confluentinc
• Source: confluentinc/agent-skills
• License: Apache-2.0

Install and usage instructions live in the source repository linked above.