◆ Apache Airflow

Organize DAGs and tasks

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 taskOrganize all DAGs related to financial reporting into a new folder called 'finance'. Move the…+
Organize all DAGs related to financial reporting into a new folder called 'finance'. Move the 'monthly_revenue_report' and 'quarterly_expenses_summary' DAGs into it, and ensure their task dependencies remain intact.
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 start moving these DAGs, can you suggest a standard naming convention and folder…+
Before I start moving these DAGs, can you suggest a standard naming convention and folder structure that would make it easier for new team members to understand the purpose of each DAG and its tasks? I want to avoid the confusion we had last quarter.
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'm trying to group our customer data DAGs into a 'customer_360' folder, but I've noticed that…+
I'm trying to group our customer data DAGs, but some tasks are shared across multiple DAGs in different functional areas.
I'm trying to group our customer data DAGs into a 'customer_360' folder, but I've noticed that several key tasks, like 'deduplicate_customer_records', are used by DAGs in both the marketing and sales folders. If I move them, I'm worried about breaking dependencies in other DAGs or creating redundant tasks. What's the best way to organize this without duplicating code or creating a tangled mess of cross-folder dependencies?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
DBecome — change the patternOur DAGs and tasks are becoming a sprawling mess, making it incredibly hard to find anything,…+
Our DAGs and tasks are becoming a sprawling mess, making it hard to find anything or understand relationships.
Our DAGs and tasks are becoming a sprawling mess, making it incredibly hard to find anything, understand dependencies, or onboard new engineers. We waste too much time just locating the right pipeline. What habit should I change in how we initially structure and name our DAGs and tasks to ensure they remain manageable and understandable as our data platform grows?
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.