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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.
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SOC 職業分類に基づく
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.
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.
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.
| name | feature-adoption-analyzer |
| description | 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. |
Understand why a shipped feature is or isn't being used.
See worked example for a complete scenario.
