Dry-runs are not paranoia for operations teams; they are contracts that your Python automation understands the same story as your stakeholders. When Stream Core Hub mentors review scripts from the Python Launchpad for Ops Teams track, they almost always ask for a dry-run flag before discussing performance tweaks.
Start by listing every side effect your script might trigger, even boring ones like touching a log file. Translate that list into explicit branches so teammates can read the intent without running anything risky. Second, capture sample output as fixtures so future edits have a baseline to diff against.
Finally, socialize the dry-run output with someone outside your role. Analysts often spot ambiguous verbs like "archive" that mean different things on different floors. The extra conversation slows you down once, then saves you from weekend reversions.