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Blameless postmortem draft: timeline, contributing factors, and actions that stick

An incident is resolved and the postmortem is due. Draft a blameless write-up from the chat logs and alerts while the details are fresh.

The prompt — copy and run it

You are a site-reliability engineer drafting a blameless incident postmortem. I will paste the alert history, the incident chat log, and the fix that was deployed.

Produce:

A) SUMMARY — what happened, user impact, duration, and how it was detected, in 4 sentences.

B) TIMELINE — timestamped events from my paste: first signal, detection, escalation, key decisions, mitigation, resolution. Mark gaps where the logs are silent.

C) CONTRIBUTING FACTORS — technical and process factors, each phrased as a condition in the system, not a person's mistake.

D) WHAT WENT WELL AND WHAT SLOWED US — detection, communication, tooling, runbooks.

E) ACTION ITEMS — each one specific, owned by a role, dated, and tagged prevent, detect faster, or recover faster. Limit to the 5 with the most impact.

Inputs: [PASTE ALERT HISTORY] · [PASTE INCIDENT CHAT LOG] · [THE FIX DEPLOYED] · [USER IMPACT DATA]

Rules: Do not invent timestamps, causes, or impact numbers — use only my paste and mark anything inferred. Keep it blameless: refer to roles, never named individuals. Verify the timeline against the raw logs before publishing. Strip customer data, credentials, and personal identifiers from anything quoted.
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How to use this prompt

  1. Copy the full prompt above with the Copy button.
  2. Fill in your inputs. Replace each bracketed placeholder with your specifics: [PASTE ALERT HISTORY][PASTE INCIDENT CHAT LOG][THE FIX DEPLOYED][USER IMPACT DATA]
  3. Paste into ChatGPT, Claude, or Gemini and run. Read the reality guardrail below before you act on the output.

Why this prompt works

Postmortems fail when they blame a person, which teaches people to hide mistakes, or when they end in vague actions nobody owns. Framing contributing factors as system conditions, building a timeline only from logs, and limiting actions to five owned, dated items tagged by type produces fixes that actually happen. Marking silent gaps shows where monitoring is missing.

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Reality guardrail: this prompt makes the model reason from data you paste — it does not source or verify facts for you. Check every claim, keep confidential data out of consumer AI tools, and follow your employer's AI-use policy.

Frequently asked

When should I use this prompt?

An incident is resolved and the postmortem is due. Draft a blameless write-up from the chat logs and alerts while the details are fresh.

Why does this prompt work?

Postmortems fail when they blame a person, which teaches people to hide mistakes, or when they end in vague actions nobody owns. Framing contributing factors as system conditions, building a timeline only from logs, and limiting actions to five owned, dated items tagged by type produces fixes that actually happen. Marking silent gaps shows where monitoring is missing.

What mistake does this prompt help you avoid?

Postmortems that blame people or end in vague actions: system-condition factors, a log-backed timeline, and five owned, dated, typed actions.

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PromptSharp prompts are drafted with AI assistance and human-reviewed. They structure how a model reasons over data you provide — they do not source or verify facts for you, and you own every output. Nothing here is financial, legal, tax, or investment advice. Never paste confidential, client, or material non-public information into consumer AI tools; follow your employer's AI-use policy. © 2026 PromptSharp.