메뉴
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.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Exposes hidden risks by identifying and ranking assumptions across desirability, feasibility, and viability categories. Use when evaluating new products or features to surface the highest-risk assumption to test first.
Designs fast, reliable validation experiments with hypothesis, method, success metric, and decision rules (Ship/Iterate/Kill). Use when you need to validate an assumption or test a product hypothesis before committing resources.
Analyzes why a shipped feature is or isn't being used, covering adoption metrics, barriers, user feedback signals, and recommended actions. Use after launching a feature to diagnose adoption issues.
Translates feature ideas into Jobs-to-Be-Done format with functional and emotional jobs, success criteria, and switching triggers. Use when reframing feature requests to understand the underlying user motivation.
Audits whether a feature or product is truly ready for launch with a structured checklist and readiness status (Ready/At Risk/Not Ready). Use before any product launch to catch critical gaps.
Shifts thinking from feature delivery to measurable user or business outcomes. Use before building a feature, during roadmap planning, or while defining success metrics to ensure work ties to real results.
| name | post-launch-learning |
| description | 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. |
Turn launches into structured learning for better future decisions.
See worked example for a complete scenario.
