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Apache Airflow Orchestration

Listed
manutej·vlatest·data

Automates complex workflows & schedules tasks across diverse data pipelines using Apache Airflow, streamlining your data processes for efficiency.

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 apache-airflow-orchestration -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 failed 1 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • No undeclared network calls: failed

    Contacts 1 host that is neither well-known infrastructure nor declared in the skill's frontmatter: airflow.apache.org.

    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.

    Hosts contacted

    airflow.apache.orgundeclared

    What it found

    • plugins/luxor-data-engineering/skills/apache-airflow-orchestration/README.md:145contacts airflow.apache.org
      curl -LfO 'https://airflow.apache.org/docs/apache-airflow/2.7.0/docker-compose.yaml'

  • 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 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.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    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/apache-airflow-orchestration/SKILL.md
Scanned
4 files, 3,617 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.
What each check looks for →

About this skill

What it does

This skill enables AI agents to automate complex workflows and schedule tasks using Apache Airflow, a platform for programmatically authoring, scheduling, and monitoring data pipelines. It covers key aspects of Airflow including DAG (Directed Acyclic Graph) development, task dependencies, dynamic workflow generation, and production deployment strategies. Essentially, it allows you to define your data processes as code, making them maintainable and version-controlled.

When to use it

  • Building and managing complex data pipelines with task dependencies.
  • Orchestrating ETL/ELT workflows across multiple systems.
  • Scheduling and monitoring batch processing jobs.
  • Creating dynamic workflows that generate tasks programmatically.
  • Deploying production-grade workflow automation.

Key capabilities

  • DAG Development: Creating Directed Acyclic Graphs to define workflow structure.
  • Operator Utilization: Using pre-built operators (Bash, Python, Email, Empty) and creating custom ones.
  • Task Dependencies: Defining the execution order of tasks using dependency operators (>>, <<).
  • Dynamic Workflows: Generating workflows programmatically in Python.
  • XCom Communication: Facilitating communication between tasks within a DAG.
  • Scheduling Patterns: Implementing various scheduling options (cron, timedelta, asset-based).

Example prompts

  • "Create an Airflow DAG to run a Python script daily at midnight."
  • "Define a task in my existing Airflow DAG that executes a bash command and depends on the successful completion of another task."
  • "Generate an Airflow workflow for orchestrating an ETL process across three different data systems."

Tips & gotchas

  • Requires familiarity with Python programming, as Airflow workflows are defined using Python code.
  • Understanding DAG concepts (dag_id, start_date, schedule) is crucial for effective use.
  • Airflow's UI provides comprehensive monitoring and troubleshooting capabilities; leverage it to identify and resolve issues.