◆ Apache Airflow

Apply Airflow best practices

This is real work, not a feature someone invented — it comes from real job ads and real questions people asked. Below are four ready AI prompts: get it done, make it easy for the next person to say yes to, work out the right move when you are stuck, and stop it coming back.

4prompts
See the prompt↓All Apache Airflow tasks

The same task, four prompts

today's deadline · the next reviewer · the stuck moment · the pattern
AExecute — do the immediate taskApply best practices to the 'daily_etl_pipeline' DAG. I need to make sure it's robust and easy…+
Apply best practices to the 'daily_etl_pipeline' DAG. I need to make sure it's robust and easy for the new team member, John, to understand when he takes over next month.
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
BImprove — make it easier to acceptBefore I merge the 'marketing_attribution_dag' into our main branch, review it against our…+
Before I merge the 'marketing_attribution_dag' into our main branch, review it against our standard best practices. I want to make sure it's idempotent, has proper task dependencies, and uses XComs effectively to pass data, so we don't have unexpected failures or data inconsistencies.
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
CDecide — diagnose the stuck momentMy 'customer_360_view_dag' is technically running and producing data, but it's a spaghetti of…+
My DAG works, but it's a mess and I'm worried about its stability.
My 'customer_360_view_dag' is technically running and producing data, but it's a spaghetti of tasks, and I'm constantly debugging small failures. The business intelligence team relies on this data daily, and I'm terrified it's going to collapse under its own weight. I suspect I've missed some fundamental best practices. What are the top two or three critical best practices I should apply immediately to stabilize this DAG and make it maintainable, and how do I prioritize them?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
DBecome — change the patternI find myself spending far too much time debugging my own DAGs weeks after I've built them, and…+
I consistently build DAGs that are hard to debug and maintain.
I find myself spending far too much time debugging my own DAGs weeks after I've built them, and new team members struggle to understand my logic. This is slowing down our feature delivery and increasing operational overhead. What habit should I change in my initial DAG design and coding workflow to ensure I'm consistently building robust, maintainable, and easily understandable DAGs from the start?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?

Questions people actually ask

honest answers, no sign-up

Every task here was seen in the real world. Someone doing the job named it, a real job ad asked for it, or a lot of people asked about it online.

If nothing real showed a task, it is not on the page. That is the whole rule.

They are the same job approached four ways, because what you need depends on where you are.

Get it done today. Make it easy for the next person to say yes to. Work out the right move when you are stuck. Learn the pattern so the job stops coming back.

For most of these jobs it can carry the heavy thinking - draft it, sort it, check it, rehearse it with you.

It cannot sit in your chair, take the blame when a number is wrong, or notice what nobody wrote down. Let it do the first 80%. Keep the last 20% that is truly yours.

No. Copy any prompt and paste it into the AI you already use. No account, no score, no wall in the way.

Any of them. The prompts describe the work rather than naming a product, so they are not tied to one assistant.

That is also why they keep working when you switch.

Change it freely. Every prompt is a starting line, not a rule.

Put in your real numbers, your real names and your real deadline. The more you make it yours, the better the answer comes back.

The tasks come from real job ads, published job data and the questions people ask in public forums.

The steps come from Apache Airflow's own documentation, with practitioner sources for the traps the manual does not mention.

Push once. Ask it to sharpen the weakest part and to say what it assumed.

Most wrong answers come from a missing detail rather than a bad prompt - tell it the thing it could not know.