数据与研究

postmortem

alirezarezvani/…e-skills

/em:postmortem — Honest analysis of what went wrong. Use after a failed launch, missed quarter, or bad hire to run a blameless 5-Whys retrospective with a change register — e.g. dissecting why the Q3 release slipped six weeks.

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安装命令

npx skills add https://github.com/alirezarezvani/claude-skills --skill postmortem

SKILL.md

/em:postmortem — Honest Analysis of What Went Wrong

Command: /em:postmortem <event>

Not blame. Understanding. The failed deal, the missed quarter, the feature that flopped, the hire that didn't work out. What actually happened, why, and what changes as a result.


Why Most Post-Mortems Fail

They become one of two things:

The blame session — someone gets scapegoated, defensive walls go up, actual causes don't get examined, and the same problem happens again in a different form.

The whitewash — "We learned a lot, we're going to do better, here are 12 vague action items." Nothing changes. Same problem, different quarter.

A real post-mortem is neither. It's a rigorous investigation into a system failure. Not "whose fault was it" but "what conditions made this outcome predictable in hindsight?"

The purpose: extract the maximum learning value from a failure so you can prevent recurrence and improve the system.

/em:postmortem — Honest Analysis of What Went Wrong

Command: /em:postmortem <event>

Not blame. Understanding. The failed deal, the missed quarter, the feature that flopped, the hire that didn't work out. What actually happened, why, and what changes as a result.


Why Most Post-Mortems Fail

They become one of two things:

The blame session — someone gets scapegoated, defensive walls go up, actual causes don't get examined, and the same problem happens again in a different form.

The whitewash — "We learned a lot, we're going to do better, here are 12 vague action items." Nothing changes. Same problem, different quarter.

A real post-mortem is neither. It's a rigorous investigation into a system failure. Not "whose fault was it" but "what conditions made this outcome predictable in hindsight?"

The purpose: extract the maximum learning value from a failure so you can prevent recurrence and improve the system.