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Kafka Stream Processing

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manutej·vlatest·backend

This skill enables real-time data transformation and analysis using Kafka Streams, useful for building reactive applications and streaming pipelines.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add manutej/luxor-claude-marketplace --skill kafka-stream-processing -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

manutej

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

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  • No pipe-to-shell installers: passed

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    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned manutej/luxor-claude-marketplace at 1861cc9720.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
1861cc9720
Manifest
plugins/luxor-data-engineering/skills/kafka-stream-processing/SKILL.md
Scanned
4 files, 3,023 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
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About this skill

kafka-stream-processing

What it does

This skill enables AI agents to process real-time data streams using Apache Kafka, facilitating efficient event-driven architectures. It allows for the transformation, filtering, and aggregation of high-volume data pipelines in production environments.

When to use it

  • Building real-time analytics dashboards that require immediate insights from incoming data feeds.
  • Implementing event sourcing patterns where system state must be reconstructed from a sequence of events.
  • Decoupling microservices by routing asynchronous messages between independent application components.
  • Performing complex stream transformations like windowing, joining, or enrichment on live data before storage.

Key capabilities

  • Integration with Apache Kafka brokers for message ingestion and ejection.
  • Stream processing logic execution within a reactive framework.
  • Support for stateful computations across continuous data flows.
  • Handling of high-throughput event streams with low latency requirements.

Example prompts

  • "Set up a Kafka stream processor to filter out invalid user sessions from the login event log in real-time."
  • "Create a pipeline that aggregates clickstream data by 1-minute windows and calculates bounce rates for the marketing dashboard."
  • "Configure an event-driven architecture where order placement messages trigger inventory updates via Kafka streams."

Tips & gotchas

Ensure you have a running Kafka cluster configured with appropriate topics before deploying stream processing logic. Be mindful of state management overhead when dealing with long-running stream applications to prevent memory leaks.