◆ Apache Airflow

Resolve DAGs not loading

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 taskAttempt to reload all DAGs in the…+
Attempt to reload all DAGs in the scheduler.
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 acceptAfter attempting to reload the DAGs, if the 'daily_sales_pipeline' DAG is still not loading,…+
After attempting to reload the DAGs, if the 'daily_sales_pipeline' DAG is still not loading, check the syntax of its Python file for common errors and report any issues found.
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
CDecide — diagnose the stuck momentA new DAG for the marketing campaign, 'campaign_launch_2024', isn't showing up in the UI even…+
A new DAG for the marketing campaign, 'campaign_launch_2024', isn't showing up in the UI even after I've placed the file in the DAGs folder.
A new DAG for the marketing campaign, 'campaign_launch_2024', isn't showing up in the UI even after I've placed the file in the DAGs folder. I've checked the file permissions, but I'm unsure if it's a syntax error in the Python file itself or an issue with the scheduler picking it up. The marketing team needs to schedule this by end of day. What's the best first step to diagnose why it's not loading?
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 spend too much time debugging why new or updated DAGs aren't loading correctly,…+
I frequently spend too much time debugging why new or updated DAGs aren't loading correctly.
I frequently spend too much time debugging why new or updated DAGs aren't loading correctly, often due to subtle syntax errors or environment issues. This delays deployment and wastes my focus. What habit should I change in my DAG development or testing process to ensure new DAGs load reliably and quickly, reducing the time spent on these basic failures?
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.