◆ Apache Airflow

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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 taskRun the 'daily_sales_report' DAG for yesterday's data, ensuring it completes by 9 AM so the…+
Run the 'daily_sales_report' DAG for yesterday's data, ensuring it completes by 9 AM so the sales team has their figures before their morning meeting.
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 kick off the 'daily_sales_report' DAG, check its dependencies and ensure all upstream…+
Before I kick off the 'daily_sales_report' DAG, check its dependencies and ensure all upstream data sources are available and validated. If any fail, automatically retry up to three times with a five-minute delay, then alert the data ops team if it still hasn't run successfully, so the sales team isn't left without their numbers.
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 'customer_segmentation' DAG failed unexpectedly this morning, right before the marketing…+
The 'customer_segmentation' DAG failed again, and I'm not sure why.
The 'customer_segmentation' DAG failed unexpectedly this morning, right before the marketing team's weekly review. The logs show a generic data parsing error, but I can't pinpoint the exact source or why it's intermittent. The marketing lead is pressing for the updated segments, and I don't want to risk running it again without understanding the root cause. What's the likely diagnosis for an intermittent data parsing error in a complex DAG, and what's the best next move to get it running reliably?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
DBecome — change the patternI've noticed we spend too much time manually intervening in DAG failures, especially for issues…+
We frequently get alerts about failed DAGs that are easily fixable but require manual intervention.
I've noticed we spend too much time manually intervening in DAG failures, especially for issues like temporary network glitches or brief database unavailability. This constant firefighting pulls us away from more strategic work. What habit should I change in how I configure DAGs to automatically handle transient failures and reduce the noise from easily recoverable errors?
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.