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Production Incident Post-Mortem

Turns an incident timeline into a blameless post-mortem that separates trigger from cause from contributing conditions, with actions that are actually assigned.

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CategoryCodingForDevelopers, OperatorsTested onClaudeChatGPT

Running it, start to finish

  1. Reconstruct the timeline from logs with real timestamps.
  2. Push the why-chain in section 3 until you reach something structural.
  3. Cut the action list to the ones with a real owner and a real date.

What you get back

The output this produces, every time.

  • Separates trigger from cause, and pushes past human error to the system that allowed it to matter.
  • Adds what-we-believed against what-was-true to the timeline, which is where response time is actually lost.
  • Sorts actions into prevent/detect/mitigate/respond and flags the ones nobody will really do.

Getting better results

Where this usually goes wrong, and how to avoid it.

  • Write the timeline from logs. not memory, then annotate it with what people believed. Memory reliably compresses the confused period, which is the part worth studying.
  • Distrust an all-prevent action list. It means the analysis over-fitted to this incident. The detect and mitigate actions are what pay off on the next, different failure.
  • Keep section 5. even when it feels awkward. Whatever contained the damage is load-bearing, and load-bearing things get removed when nobody knows they are.

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Written for The AI University. Every prompt in this library is original work — authored, tested and revised here, not collected from elsewhere. 365 of them, free with an account.