◆ Apache Airflow

Return results from PythonOperator

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 taskExecute the Python script 'calculate_monthly_revenue.py' and return the final calculated…+
Execute the Python script 'calculate_monthly_revenue.py' and return the final calculated revenue figure for October 2023.
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 acceptRun the daily fraud detection script. If the script returns any transaction IDs flagged as…+
Run the daily fraud detection script. If the script returns any transaction IDs flagged as 'high-risk', format them as a comma-separated string and include them in the email alert to the finance team, otherwise return 'No high-risk transactions detected'.
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 inventory reconciliation script is returning an empty list, but I know there are mismatches…+
The inventory reconciliation script is returning an empty list, but I know there are mismatches that need to be reported.
The inventory reconciliation script is returning an empty list, but I know there are mismatches that need to be reported to operations by 9 AM. I suspect the database query isn't pulling the right date range, or maybe the script's filtering logic is too aggressive. I can't tell which without digging in. What's the quickest way to debug the script's output and confirm the data it's actually processing?
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 struggle to get useful, structured data out of my Python tasks for downstream use,…+
I frequently struggle to get useful, structured data out of my Python tasks for downstream use.
I frequently struggle to get useful, structured data out of my Python tasks for downstream use, especially when there are multiple outputs or complex objects. This leads to brittle parsing in subsequent steps. What habit should I change to ensure my Python tasks consistently return easily consumable, unambiguous results for the next stage of the workflow?
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.