Free Agent Skill
Postmortem / Retro
Extract a specific, reusable lesson after something succeeded or failed, following the principle that pain plus reflection equals progress — outcomes only compound into better judgment if they're deliberately converted into a written, checkable lesson rather than just felt and moved past
Use when: The user is wrapping up a project, a decision has resolved (especially one logged earlier via decision-memo or calibrated-forecasting), something went notably well or badly, or /retro is invoked directly. This is the feedback loop that makes future decisions sharper — turn real outcomes into a durable lesson, not just another conversation
Install this skill
Cursor, Claude Code and Codex
- Create a folder named `postmortem-retro`.
- Save the skill content below as `SKILL.md` inside that folder.
- Place the folder in `.cursor/skills/` for Cursor or `.claude/skills/` for Claude Code (or the equivalent path for Codex).
- Describe the task normally. Your agent loads the skill when its description matches.
Use this in ChatGPT or Claude (no code)
- Copy or download SKILL.md from this page (or put it in a folder named
postmortem-retro). - Zip that folder so the archive contains
postmortem-retro/SKILL.md. - In ChatGPT (Business, Enterprise, Edu or Healthcare), upload the zip under workspace Skills. In Claude.ai, add a Skill from the zip where your plan supports Skills.
- Describe the task in plain language — the skill loads when its description matches.
---
name: postmortem-retro
description: Extract a specific, reusable lesson after something succeeded or failed, following the principle that pain plus reflection equals progress — outcomes only compound into better judgment if they're deliberately converted into a written, checkable lesson rather than just felt and moved past. Use whenever the user is wrapping up a project, a decision has resolved (especially one logged earlier via decision-memo or calibrated-forecasting), something went notably well or badly, or /retro is invoked directly. This is the feedback loop that makes future decisions sharper — turn real outcomes into a durable lesson, not just another conversation.
---
# Postmortem / Retro
An outcome without a deliberate retro teaches nothing, or teaches the
wrong thing — people are prone to over-crediting skill for good outcomes
and bad luck for bad ones. This skill forces the harder, more useful
version of the accounting.
## The technique
**1. State the outcome plainly, without spin.** What actually happened,
in one or two sentences, stated the same way it would be described to
someone with no stake in it looking good.
**2. Separate decision quality from outcome quality.** A good decision can
have a bad outcome (bad luck) and a bad decision can have a good outcome
(good luck). Ask explicitly: given what was actually known *at the time*
(not what's known now), was this a good decision? This is the single most
commonly skipped step, and skipping it is what makes people learn the
wrong lesson from a good result reached the wrong way, or punish
themselves for a good call that got unlucky.
**3. Check any prior forecast or premortem against reality.** If there
was a `calibrated-forecasting` estimate or a `premortem` list of risks
made beforehand, check it explicitly: what did it get right, what did it
miss, and why? An unchecked prediction can't improve future ones.
**4. Find the one thing that would change next time**, not five vague
ones. Push past the first, easy lesson ("communicate more") to something
specific enough to actually change behavior ("send the draft two days
earlier so there's time for one real revision round," not "be more
proactive").
**5. Name what to keep doing.** Retros that only hunt for what went wrong
miss reinforcing what worked — ask explicitly what should be repeated
deliberately, not just what should stop.
**6. Write the durable lesson down.** A lesson that stays only in this
conversation does not compound. If it is genuinely reusable rather than a
one-off circumstance, add it to the user's own dated decision or learning log.
## Output
The plain outcome, the decision-quality-vs-outcome-quality read, one
specific lesson to change, one thing to keep doing, and a concise durable
lesson to save when appropriate.
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