Query, manage, and troubleshoot Apache Airflow DAGs, runs, tasks, and system configuration.
Run in your terminal
Supports 30+ commands across DAG inspection, run management, task logging, configuration queries, and direct REST API access
Manage multiple Airflow instances with persistent configuration; auto-discover local and Astro deployments
Trigger DAG runs synchronously (wait for completion) or asynchronously, diagnose failures, clear runs for retry, and access task logs with retry/map-index filtering
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionairflowExecute the skills CLI command in your project's root directory to begin installation:
Package manager
npx skills add https://github.com/astronomer/agents --skill airflowFetches airflow from astronomer/agents and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate airflow. Access via /airflowin your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Package manager
npx skills add https://github.com/astronomer/agents --skill airflowWorks with
2
total installs
2
this week
302
GitHub stars
0
upvotes
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
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sickn33/antigravity-awesome-skills
myzy-ai/dokie-ai-ppt
shadowcz007/skills
airflow reduced setup friction for our internal harness; good balance of opinion and flexibility.
airflow has been reliable in day-to-day use. Documentation quality is above average for community skills.
Registry listing for airflow matched our evaluation — installs cleanly and behaves as described in the markdown.
We added airflow from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
airflow fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Solid pick for teams standardizing on skills: airflow is focused, and the summary matches what you get after install.
We added airflow from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
airflow has been reliable in day-to-day use. Documentation quality is above average for community skills.
I recommend airflow for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Keeps context tight: airflow is the kind of skill you can hand to a new teammate without a long onboarding doc.
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