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Decision Autopsy

核心功能是避开结果偏见,从过程维度复盘过往重大决策,通过信息、流程测试等框架,区分决策质量与结果,量化运气占比,提炼唯一可落地的流程改进。典型场景:团队因失败/成功结果误判决策质量时使用。输入需决策详情、当时可获知信息、实际结果;输出为结构化报告,含决策与结果判定、可追溯账本、运气分析等内容。

mohitagw15856
mohitagw15856
热度 10.47
最近更新 2026-07-16 22:31:54
分类:协作依赖:纯提示词应用场景:报告撰写 / 会议纪要 / 工作流

安装技能

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执行命令安装此 skill: npx secure-skills-cli add decision-autopsy
Metadata
name
decision-autopsy
description
Judge a past decision by its PROCESS, not its outcome — because good decisions lose and bad decisions win, and teams that can't tell the difference learn the wrong lessons. Use when reviewing a big call after the fact (a bet that failed, a pass that haunts, a hire, a pivot) and the room is about to conclude 'it failed so it was wrong.' Produces a process-forensics report: what was knowable then, the quality grade of the decision as-made, the luck accounting, and the ONE process change worth keeping.
homepage
https://mohitagw15856.github.io/pm-claude-skills/skill/decision-autopsy.html
metadata
{"openclaw":{"emoji":"⚔️"}}

Decision Autopsy

Outcome bias is the strongest bias in organisational memory: the bet that failed becomes "obviously reckless," the coin-flip that landed becomes "visionary." The autopsy separates the two questions that always get merged: was it a good decision? and did it get a good outcome? — because only the first is under anyone's control next time.

Required Inputs

  • The decision — what was decided, when, by whom, and what the live alternatives were.
  • What was knowable at the time — the information, constraints, and time pressure as of the decision date. Be strict: things learned afterward go in a separate pile, and the autopsy will police the boundary.
  • The outcome — what actually happened, so the luck accounting has something to account.

The Forensic Frames

  • The information test: given only what was knowable then, what would a calibrated outsider have chosen? (The autopsy answers this before re-examining the outcome, to keep hindsight out of the grade.)
  • The process test: were alternatives really generated? Was disconfirming evidence sought or only tolerated? Was the reversibility of the choice priced in? Was a kill-criterion set?
  • The luck accounting: decompose the outcome into decision quality vs. variance — what portion of the result would replay differently if the world rolled again?
  • The lesson filter: the only lessons worth keeping are process lessons ("we never priced reversibility") — outcome lessons ("don't bet on X") overfit to one roll of the dice.

Output Format

  1. The two verdicts, separated — Decision: 🟢 sound / 🟡 flawed / 🔴 negligent as made. Outcome: good / bad / mixed. State them side by side; the whole point is that they can disagree.
  2. The knowability ledger — table: fact | knowable then? | actually known? | changed the call? Hindsight contamination gets flagged explicitly ("this entered the story after the fact").
  3. The luck accounting — one honest paragraph: what fraction of this outcome was variance, with the reasoning shown.
  4. The one process change — a single, named, repeatable change to how decisions like this get made ("every >$100k bet gets a written kill-criterion before commitment"). One. Teams adopt one; they file lists.
  5. The replay line — "facing the same information again, the right call would be ___" — the sentence that inoculates the team against both regret and false confidence.

Quality Checks

  • The decision grade was assigned from the knowability ledger BEFORE outcome discussion, and the report's structure shows it
  • At least one hindsight contamination is caught and named — reviews without any are usually not looking
  • The luck paragraph commits to a rough proportion, with reasoning — "some luck was involved" is evasion
  • The process change is executable next quarter and testable ("did we do it?"), not a value statement
  • If the decision was 🟢 and the outcome bad, the report says the uncomfortable sentence plainly: "do it again"

Anti-Patterns

  • Do not let the outcome leak into the grade — a bad result may not appear as evidence of a bad decision anywhere in the report
  • Do not run an autopsy as a trial — no verdicts on people; the unit of analysis is the process that any competent person was embedded in
  • Do not conclude "we were unlucky" without the ledger to earn it — luck is the residual after process is examined, never the headline
  • Do not extract more than one lesson — the second-best lesson dilutes the best one
  • Do not autopsy decisions younger than their outcome — if the result isn't actually in yet, this is a premortem's job