◆ Apache Airflow

Remove default example 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 rid of the example DAGs that came with the data orchestration installation. They're…+
Get rid of the example DAGs that came with the data orchestration installation. They're cluttering the UI and confusing the team.
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 we go live with the new data pipeline, make sure all the default example DAGs are gone.…+
Before we go live with the new data pipeline, make sure all the default example DAGs are gone. We need a clean environment for production, and I don't want anyone accidentally triggering an old test. Confirm they're fully removed and won't reappear.
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
CDecide — diagnose the stuck momentI've deleted the example DAGs three times now, but they keep reappearing, making the UI messy…+
The team just started building new DAGs, but the old example ones keep showing up in the UI, even after I thought I deleted them.
I've deleted the example DAGs three times now, but they keep reappearing, making the UI messy and confusing the new hires. I'm worried we're not actually cleaning up the environment, and it's making us look disorganized. What's the real way to permanently remove these, and what am I missing about their persistence?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
DBecome — change the patternWe consistently lose time and introduce confusion whenever we provision a new Airflow instance…+
Every time we set up a new Airflow instance, we waste a day manually cleaning up example DAGs.
We consistently lose time and introduce confusion whenever we provision a new Airflow instance because of the default example DAGs. It's a recurring setup headache. What habit should we change or automate so that new environments are clean and ready for our actual work from day one, without this manual cleanup step?
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.