◆ Apache Airflow

Pass data between Airflow tasks

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 taskTake the output from the 'extract_new_users' task, which is a list of user IDs, and pass it to…+
Take the output from the 'extract_new_users' task, which is a list of user IDs, and pass it to the 'enrich_user_profiles' task. Ensure the data type is preserved.
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 acceptWhen passing the user IDs from the 'extract' task to the 'enrich' task, make sure it can handle…+
When passing the user IDs from the 'extract' task to the 'enrich' task, make sure it can handle a list of up to a million IDs without performance issues. If the list is empty, the 'enrich' task should be skipped. Also, log the number of IDs passed for auditing.
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 'enrich_user_profiles' task is failing, saying it received an empty list, but I'm certain…+
The 'enrich_user_profiles' task is failing, saying it received an empty list, but I'm certain the 'extract_new_users' task produced data.
The 'enrich_user_profiles' task is failing, saying it received an empty list, but I'm certain the 'extract_new_users' task produced data. I'm afraid there's a serialization issue or a silent failure in the data passing mechanism. I can't tell the product team we lost their new user data. What just happened and what's the best next move to recover the data and fix the pipeline?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
DBecome — change the patternI frequently encounter issues with data not being correctly passed between tasks, leading to…+
I frequently encounter issues with data not being correctly passed between tasks, leading to downstream failures.
I frequently encounter issues with data not being correctly passed between tasks, leading to downstream failures and hours of debugging. It's hard to trace where the data is getting lost or corrupted. What habit should I change to ensure reliable data handoffs and prevent these frustrating 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.