◆ Apache Airflow

Add new DAGs to Airflow

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 taskWe've got the new 'marketing_attribution_dag.py' file ready. Can you deploy it to the…+
We've got the new 'marketing_attribution_dag.py' file ready. Can you deploy it to the production environment and ensure it's scheduled to run daily at 3 AM UTC? The marketing team is waiting on this data for their weekly performance review on Monday.
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 deploying the new 'inventory_forecast_dag.py', can you scan its dependencies to make…+
Before deploying the new 'inventory_forecast_dag.py', can you scan its dependencies to make sure it won't conflict with existing data sources or overload our database during its scheduled run? The operations team needs accurate numbers by end of day, and I don't want to break their existing reports.
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
CDecide — diagnose the stuck momentI'm deploying the new 'customer_journey_dag' for the product team, but the upstream CRM data…+
I'm about to deploy a new 'customer_journey_dag', but the data source schema changed unexpectedly last week.
I'm deploying the new 'customer_journey_dag' for the product team, but the upstream CRM data source schema changed last week, and I haven't had a chance to fully adapt the DAG. I'm afraid if I deploy it, it will fail immediately and generate bad data, delaying the product release. Should I push it with a known risk, or hold off and explain the delay to the product manager? What's the best way to handle this without causing a bigger mess?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
DBecome — change the patternEvery time we deploy a new DAG, it feels like we discover unforeseen conflicts or resource…+
New DAG deployments frequently cause unexpected downstream failures or resource contention.
Every time we deploy a new DAG, it feels like we discover unforeseen conflicts or resource issues only after it goes live, leading to urgent fixes. I'm losing credibility with the business teams because of these disruptions. What habit can I change to better anticipate these problems during the deployment process, so new DAGs integrate smoothly without breaking existing workflows?
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.