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Debugging Dags

Checked
astronomer·vlatest·testing·Apache-2.0

Identifies and resolves data pipeline errors within Astronomer DAGs using automated analysis and suggested fixes.

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 astronomer/agents --skill debugging-dags -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

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

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

    Only contacts known or declared hosts: pypi.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

    pypi.orgpackage registry

  • 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/debugging-dags/SKILL.md
Scanned
1 file, 125 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 debugging-dags skill helps identify and resolve issues in Directed Acyclic Graphs (DAGs), commonly used in workflow automation systems like Apache Airflow. It provides tools to trace execution paths, detect errors, and analyze dependencies between tasks.

When to use it

  • When a DAG fails unexpectedly during execution.
  • To understand why certain tasks are not running as expected.
  • While troubleshooting complex workflows with multiple interdependent tasks.
  • To optimize performance by identifying bottlenecks or redundant steps in the graph.

Key capabilities

  • Task dependency analysis
  • Execution path tracing
  • Error detection and resolution
  • Performance optimization suggestions

Example prompts

  • "Find why task 'data_load' is failing in my DAG."
  • "Trace the execution path of this DAG to identify bottlenecks."
  • "Analyze dependencies between tasks in this workflow."

Tips & gotchas

  • Ensure your DAGs are properly structured and documented for accurate analysis.
  • This skill works best with well-defined workflows; highly dynamic or unstructured graphs may yield less useful results.