◆ Apache Airflow

Import local Python modules in DAGs

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 taskGet the new data quality checks from the `data_validation` folder into our `daily_etl_dag` so…+
Get the new data quality checks from the `data_validation` folder into our `daily_etl_dag` so they run tonight and flag any issues before the marketing report goes out tomorrow morning.
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 push these new data quality modules to production, make sure they're robust. Can you…+
Before I push these new data quality modules to production, make sure they're robust. Can you refactor the imports to handle potential circular dependencies or missing files gracefully, so a single error doesn't crash the whole `daily_etl_dag`?
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` module is failing to import in the `hourly_dashboard_refresh` DAG,…+
The new `customer_segmentation` module is failing to import in our `hourly_dashboard_refresh` DAG, but it works fine locally.
The `customer_segmentation` module is failing to import in the `hourly_dashboard_refresh` DAG, but it works fine on my machine. I'm afraid I've missed some subtle environment difference, and I can't afford to break the dashboard for the sales team. What's the most likely cause, and how should I debug this without impacting production?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
DBecome — change the patternI keep losing hours trying to get local Python modules to import correctly in our DAGs, often…+
I keep losing time when local Python modules fail to import in DAGs due to environment mismatches or path issues.
I keep losing hours trying to get local Python modules to import correctly in our DAGs, often due to subtle path differences or missing dependencies in the data orchestration environment. This always delays critical data pipelines. What habit should I change to prevent these frustrating import errors from recurring?
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.