◆ Apache Airflow

Create a DAG via Airflow UI

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 taskCreate a new DAG named 'hourly_data_pull' that runs every hour. It should have a single…+
Create a new DAG named 'hourly_data_pull' that runs every hour. It should have a single PythonOperator task named 'fetch_api_data' that executes a Python script located at '/opt/scripts/fetch_data.py' within our data pipeline orchestration system.
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 create this new DAG, can you help me think through the best practices for structuring…+
Before I create this new DAG, can you help me think through the best practices for structuring a simple hourly data pull? I want to make sure I include proper error handling, logging, and idempotency from the start, so it's not a headache later.
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 create a new DAG for a critical daily report that needs to run after two upstream…+
I'm trying to create a new DAG for a critical daily report, but I'm unsure about the best way to define the schedule and dependencies.
I'm trying to create a new DAG for a critical daily report that needs to run after two upstream data pipelines complete, but also strictly by 6 AM every weekday. I'm afraid of setting the schedule wrong and either missing the deadline or running before the data is ready. What's the best way to define this complex schedule and dependency in a new DAG to ensure both conditions are met reliably?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
DBecome — change the patternCreating new DAGs often takes longer than it should because I'm always reinventing the wheel…+
Creating new DAGs often takes longer than it should because I'm always reinventing the wheel for common patterns.
Creating new DAGs often takes longer than it should because I'm always reinventing the wheel for common patterns like daily extracts, error handling, or notification systems. I spend too much time looking up syntax or best practices for each new DAG. What habit should I change in how I approach new DAG creation to leverage existing patterns or templates, making the process faster and more consistent?
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.