◆ Apache Airflow

Keep Airflow tasks running continuously

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

The same task, four prompts

today's deadline · the next reviewer · the stuck moment · the pattern
AExecute — do the immediate taskThe 'daily_customer_segmentation' DAG stopped running last night. Get it running again and…+
The 'daily_customer_segmentation' DAG stopped running last night. Get it running again and ensure it processes all missed runs from yesterday, December 14th.
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 restarting the 'daily_customer_segmentation' DAG, check its logs for the last 24 hours…+
Before restarting the 'daily_customer_segmentation' DAG, check its logs for the last 24 hours to identify the cause of the stoppage. If it's a known resource issue, try to allocate more memory before restarting. If it's an unknown error, just restart but flag it for me to investigate.
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
CDecide — diagnose the stuck momentThe critical 'fraud_detection_pipeline' DAG is showing intermittent failures and restarts every…+
The 'fraud_detection_pipeline' DAG is showing intermittent failures and restarts, but I can't pinpoint a consistent error in the logs.
The critical 'fraud_detection_pipeline' DAG is showing intermittent failures and restarts every few hours, but the logs don't point to a consistent error – sometimes it's a timeout, sometimes a connection reset. The compliance team is breathing down my neck for continuous monitoring. I'm worried it's a deeper, systemic issue I'm not seeing. What's the most likely root cause here, and what's my best immediate action to stabilize it?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
DBecome — change the patternI often discover DAGs have stopped or are stuck hours after the fact, leading to delayed data…+
I frequently find DAGs have stopped or are stuck without clear alerts, leading to delayed data for stakeholders.
I often discover DAGs have stopped or are stuck hours after the fact, leading to delayed data for the executive dashboards and angry calls from the business. My current monitoring isn't catching these issues proactively enough. What habit can I change to ensure I'm alerted to these stoppages much faster, before they impact the business?
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.