◆ Apache Airflow

Implement Airflow governance with cluster policies

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
See the prompt↓All Apache Airflow tasks

The same task, four prompts

today's deadline · the next reviewer · the stuck moment · the pattern
AExecute — do the immediate taskSet up a cluster policy for the new financial reporting DAGs so that new tasks default to a…+
Set up a cluster policy for the new financial reporting DAGs so that new tasks default to a 30-minute timeout and only use the 'reporting-medium' resource queue.
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 roll out cluster policies for all new DAGs, define a policy that balances resource…+
Before we roll out cluster policies for all new DAGs, define a policy that balances resource efficiency with developer agility. I need to make sure teams can still iterate quickly without accidentally bringing down the cluster with runaway jobs. How do we ensure critical DAGs get priority without stifling experimentation?
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 new analytics team just pushed a DAG that spun up 50 high-CPU tasks, starving our core data…+
The new analytics team just pushed a DAG that spun up 50 high-CPU tasks, starving our core data pipelines.
The new analytics team just pushed a DAG that spun up 50 high-CPU tasks, starving our core data pipelines. I'm afraid to hard-code limits because they're still figuring out their workflows, but we can't have this happen again. I can't tell them to stop innovating. What's the fastest way to contain future rogue DAGs without blocking their development entirely, and what's the best next conversation to have with their lead?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
DBecome — change the patternNew teams keep deploying DAGs that consume too many resources or run too long, causing outages.…+
New teams keep deploying DAGs that consume too many resources or run too long, causing outages.
New teams keep deploying DAGs that consume too many resources or run too long, causing outages. I spend too much time firefighting these incidents and then manually adjusting individual DAGs. What habit should I change to prevent these resource conflicts proactively, without becoming a bottleneck for every new deployment?
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.