◆ Apache Airflow

Backup and export Airflow 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

The same task, four prompts

today's deadline · the next reviewer · the stuck moment · the pattern
AExecute — do the immediate taskExport the workflow definitions and task logs for the 'monthly_billing_report' workflow from…+
Export the workflow definitions and task logs for the 'monthly_billing_report' workflow from the last 30 days to the shared S3 bucket 'workflow-backups' by end of day Friday.
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 audit next month, make it easy to find and verify the exact DAG code and task…+
Before the audit next month, make it easy to find and verify the exact DAG code and task execution details for all financial reporting DAGs. Ensure the export includes version history and parameters used for each run, not just the latest definition.
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 need to revert the 'customer_segmentation' DAG to a version from last Tuesday because of a…+
I need to revert a DAG to an older version, but I'm worried about losing critical configuration changes made since then.
I need to revert the 'customer_segmentation' DAG to a version from last Tuesday because of a bad data transformation, but I'm worried about losing critical configuration changes for the new marketing campaign that were added yesterday. I can't tell the marketing lead we might lose their updates. What's the best way to safely roll back the code without overwriting the recent configuration, and how do I verify it before going live?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
DBecome — change the patternRecovering from accidental DAG changes or data issues is always a scramble, leading to lost…+
Recovering from accidental DAG changes or data issues is always a scramble.
Recovering from accidental DAG changes or data issues is always a scramble, leading to lost time and missed data SLAs. We often don't have a clear, reliable way to restore. What habit should I change to ensure we consistently have robust, quickly accessible backups for all critical DAGs and their related data, making recovery predictable?
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.