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Managed Airflow Recommendations

Gives data-backed tuning and reliability advice for Google Cloud Managed Airflow environments using telemetry scripts.

Source Download skill
Repository
google/skills
Stars
21,782
Licence
Apache-2.0
Files
11 files

What it does

When installed, the agent can review a running Google Cloud Managed Service for Apache Airflow (formerly Cloud Composer) environment. It reads the environment's configuration with gcloud, runs bundled Python scripts that pull CPU, memory, disk, restart and DAG parsing metrics from Cloud Monitoring, and then suggests scaling and tuning changes based on that data rather than guessing. It is designed to avoid recommending a bigger size by default.

Great for

  • Data engineers running a Managed Airflow environment who want to check sizing and stability
  • Platform teams reviewing scheduler or worker restarts and memory pressure
  • Anyone asking for a health check or best-practice review of an existing Airflow environment

Skip it if

  • Troubleshooting a single failed DAG or task run, which the skill explicitly excludes
  • Writing or migrating DAG code, which is out of scope and could be done poorly without the right tools
  • Environments you do not have read access to, since the scripts need monitoring and configuration permissions and will fail without them

Try asking

  • “Our Airflow environment in project analytics-prod, region europe-west1, keeps restarting. What is causing it and what should we change?”
  • “Review the CPU and memory usage of the workers in my Composer environment over the past 7 days and recommend sizing.”
  • “Check the DAG parsing times for environment etl-main and tell me if the scheduler is a bottleneck.”
  • “Give me a health summary and best-practice recommendations for my Managed Airflow environment.”
Step-by-step guide: how to use Managed Airflow RecommendationsInstall, first run, examples, tips and FAQs

Install in 3 steps

  1. 1

    Download the skill

    The ZIP contains the 11 original files, the Apache-2.0 licence and an attribution note.

    managed-airflow-recommendations.zip
  2. 2

    Add it to your agent

    Claude Code (all projects)

    mkdir -p ~/.claude/skills && unzip managed-airflow-recommendations.zip -d ~/.claude/skills/

    Claude Code / Agent SDK (this project only)

    mkdir -p .claude/skills && unzip managed-airflow-recommendations.zip -d .claude/skills/

    Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.

  3. 3

    Ask for the task

    No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.

Get better results

  • Give the agent the environment name, project ID, region and a clear timeframe (for example past 24 hours or past 7 days) so the metrics match the incident you care about.
  • Ask the agent to show the data behind each recommendation; the skill asks it not to generalise without metrics.
  • Run the scripts yourself with --help first to confirm what they query before letting the agent execute them against a production project.

What's inside

  • references/attribution.md
  • references/gcloud_reference.md
  • scripts/dag_parsing_stats.py
  • scripts/environment_health.py
  • scripts/lib/flagutils.py
  • scripts/lib/monitoring.py
  • scripts/workload_cpu_usage.py
  • scripts/workload_disk_usage.py
  • scripts/workload_memory_usage.py
  • scripts/workload_restarts.py
  • SKILL.md

Redistributed unmodified from google/skills under the Apache-2.0 licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.