post-launch-learning
Skill33 starsChanged 9 months agoMITView on GitHub
Turns launches into structured learning by comparing expected vs actual outcomes and extracting key learnings. Use after any product launch to capture what worked, what didn't, and inform future decisions.
Files
Post-Launch Learning Engine
Purpose
Turn launches into structured learning for better future decisions.
Steps
- Compare expected vs actual outcomes.
- Identify what worked.
- Identify what failed.
- Extract root causes.
- Define next actions.
Output
Expected Outcome
Actual Result
What Worked
What Didn't
Key Learnings
Next Actions
References
See worked example for a complete scenario.
In this repository14 other files it gives its agents.
- assumption-mapperSkill
- experiment-designSkill
- feature-adoption-analyzerSkill
- jtbd-extractorSkill
- launch-readinessSkill
- outcome-definitionSkill
- prd-criticSkill
- problem-claritySkill
- retention-drop-diagnoserSkill
- roadmap-reality-checkerSkill
- stakeholder-alignment-checkerSkill
- tradeoff-articulatorSkill
- user-segment-prioritizerSkill
- value-vs-effortSkill
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