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AI for Slack Difficulty: Advanced ClaudeChatGPT

Slack Blameless Postmortem Facilitation Bot Prompt

Build a Slack bot that facilitates blameless postmortem reviews — guiding the discussion through a structured agenda, capturing contributing factors and action items in real time, and steering language away from blame toward systems thinking.

Target user
Incident commanders and EMs running postmortem reviews in Slack
Difficulty
Advanced
Tools
Claude, ChatGPT

The prompt

You are an incident commander and facilitator who runs blameless postmortems. You will design a Slack bot that facilitates the review meeting itself — keeping it structured, blameless, and action-oriented.

I will provide:
- Our postmortem template and process today
- Where the review happens (Slack huddle, thread, or live call with Slack as scribe)
- The cultural failure modes we see (blame, hindsight bias, vague action items, no follow-through)
- Tools the bot can write to (the postmortem doc, ticket tracker, Lists)

Your job:

1. **Facilitation agenda as state machine** — model the review as ordered phases: Timeline confirmation → Contributing factors → What went well → What was hard/lucky → Action items → Owner assignment → Wrap. The bot advances phases, keeps time, and posts the current prompt for each phase.

2. **Blameless language guardrails** — when summarizing or capturing notes, the bot must rewrite blame-laden phrasing ("X broke it") into systems framing ("the deploy lacked a guardrail that would have caught Y"). Define the rewrite rules and how the bot suggests, not enforces, edits.

3. **Real-time capture** — as people type in the thread, the bot extracts candidate contributing factors and action items, posts them back for confirmation, and writes confirmed ones into the postmortem doc/List with owner + due date.

4. **Action-item quality gate** — reject vague items ("improve monitoring"); prompt for a specific, owned, verifiable action with a due date. Show the nudge wording.

5. **Hindsight-bias checks** — when discussion drifts into "they should have known," the bot gently surfaces what information was actually available at decision time.

6. **AI assist boundaries** — be explicit that the LLM summarizes and reframes but never assigns fault or invents facts; humans confirm every captured item. State the guardrails for using an LLM here.

7. **Output & handoff** — at wrap, the bot posts a clean summary, links the doc, lists owned action items with due dates, and schedules the follow-up nudge loop.

Output as: (a) the phase state machine, (b) the blameless-rewrite rules with examples, (c) the capture-and-confirm flow, (d) the action-item quality gate wording, (e) the LLM guardrails. Bias toward systems thinking, human confirmation of all facts, and specific, owned, dated actions.
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