Airflow

Name: Airflow
Author: astronomer

Checked

astronomer·vlatest·data·Apache-2.0

Airflow orchestrates complex data pipelines by scheduling tasks, ensuring efficient workflow execution and reliable data processing.

Repository

Install on your platform

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

Run this in your project

terminal

$ npx skills add astronomer/agents --skill airflow -a claude-code

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

astronomer

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 28 Sept 2026

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.
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 astronomer/agents at cbe1141f54.
The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit: cbe1141f54
Manifest: skills/airflow/SKILL.md
Scanned: 3 files, 475 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

The Airflow skill enables AI agents to interact with Apache Airflow, a platform used to programmatically author, schedule, and monitor workflows. It allows users to define complex data pipelines, manage dependencies between tasks, and execute them on a schedule or manually.

When to use it

Automating ETL processes for data engineering teams
Orchestrating machine learning model training and deployment workflows
Scheduling and monitoring batch jobs in cloud environments

Key capabilities

Define and schedule DAGs (Directed Acyclic Graphs)
Monitor task execution status and logs
Integrate with various data sources and tools like databases, APIs, and cloud storage

Example prompts

"Create a DAG that extracts data from a PostgreSQL database and loads it into S3 every hour."
"Monitor the status of all active tasks in my Airflow instance and alert me if any fail."
"Generate a report showing the execution time of each task in the last 7 days."

Tips & gotchas

Ensure your AI agent has access to an Airflow instance and proper authentication credentials.
Be mindful of DAG dependencies and scheduling intervals to avoid conflicts or redundant executions.