◆ Apache Airflow

Identify Airflow production issues

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 taskFind the DAG run that failed last night for the customer analytics pipeline. Tell me the…+
Find the DAG run that failed last night for the customer analytics pipeline. Tell me the specific task that broke and show me the logs for that task so I can see the error message. We need to know why it didn't update the dashboard data for the morning review.
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 the next customer analytics pipeline runs, make it easier to spot failures. Highlight…+
Before the next customer analytics pipeline runs, make it easier to spot failures. Highlight the most common failure points, like database connection errors or S3 access issues, and show me the logs directly related to those. I need to quickly see if it's a data source problem or a code bug.
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 customer sentiment DAG keeps failing every Friday, but runs fine the rest of the week. I'm…+
The customer sentiment DAG has been failing intermittently for a week, but only on Fridays, and I can't figure out why it's not consistent.
The customer sentiment DAG keeps failing every Friday, but runs fine the rest of the week. I'm afraid I'll miss a critical update before the executive meeting. I can't tell if it's a data volume issue, a specific upstream service, or something with the Friday data pull. What's the most likely cause, and what's the first thing I should check to diagnose this Friday-only failure?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
DBecome — change the patternI'm constantly losing time manually sifting through logs to pinpoint why DAGs fail, especially…+
I spend too much time manually digging through logs to find the root cause of DAG failures across different teams.
I'm constantly losing time manually sifting through logs to pinpoint why DAGs fail, especially when it's an intermittent issue affecting multiple teams. What habit should I change in how I approach debugging to more quickly identify and resolve the root cause of these production issues, instead of just reacting to each alert individually?
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.