◆ Apache Airflow

Import local modules into 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 local 'data_transformations.py' module imported into my 'daily_reporting_dag' so I can…+
Get the local 'data_transformations.py' module imported into my 'daily_reporting_dag' so I can use its functions in the PythonOperator tasks. I need this running by end of day Friday for the new dashboard.
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 this 'customer_segmentation_dag' to production, ensure all the necessary local…+
Before I push this 'customer_segmentation_dag' to production, ensure all the necessary local utility scripts, like 'db_utils.py' and 'api_helpers.py', are correctly imported and accessible to its tasks. Make sure it won't fail due to a missing dependency when it runs on the scheduler.
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
CDecide — diagnose the stuck momentMy 'sales_forecast_dag' just failed in production because it couldn't find…+
My DAG failed because it couldn't find a module, but it was right there.
My 'sales_forecast_dag' just failed in production because it couldn't find 'model_inference.py', even though I've confirmed it's in the same directory as the DAG file. The data science team is waiting on these outputs. I'm afraid I've misunderstood how the scheduler picks up local dependencies. What's the most likely reason for this 'ModuleNotFoundError' and what's the quickest way to fix it without breaking other DAGs?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
DBecome — change the patternI'm constantly running into issues where local Python modules I've written for data processing…+
I keep struggling with local modules not being found in production environments.
I'm constantly running into issues where local Python modules I've written for data processing or API calls aren't found when my DAGs run on the production scheduler, even after they pass local testing. This is costing us critical time in getting new data pipelines deployed. What habit should I change in how I structure or deploy my DAGs and their local dependencies to prevent these 'ModuleNotFoundError' headaches?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?

Questions people actually ask

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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.