Overview
- Skill Key
- alirezarezvani/product-discovery
- Author
- alirezarezvani
- Source Repo
- openclaw/skills
- Version
- -
- Source Path
- skills/alirezarezvani/product-discovery
- Latest Commit SHA
- 17265eec8bcc4c5e2b8554bfb8c7483c4fc3ab56
Use when validating product opportunities, mapping assumptions, planning discovery sprints, or testing problem-solution fit before committing delivery resources.
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直接复制以下提示词,发送给你的 AI 助手即可完成安装。
请先检查是否已安装 SkillHub 商店,若未安装,请根据 https://skillhub-1388575217.cos.ap-guangzhou.myqcloud.com/install/skillhub.md 安装 SkillHub 商店,然后安装 product-discovery 技能。 若已安装,则直接安装 product-discovery 技能。
# Product Discovery Run structured discovery to identify high-value opportunities and de-risk product bets. ## When To Use Use this skill for: - Opportunity Solution Tree facilitation - Assumption mapping and test planning - Problem validation interviews and evidence synthesis - Solution validation with prototypes/experiments - Discovery sprint planning and outputs ## Core Discovery Workflow 1. Define desired outcome - Set one measurable outcome to improve. - Establish baseline and target horizon. 2. Build Opportunity Solution Tree (OST) - Outcome -> opportunities -> solution ideas -> experiments - Keep opportunities grounded in user evidence, not internal opinions. 3. Map assumptions - Identify desirability, viability, feasibility, and usability assumptions. - Score assumptions by risk and certainty. Use: ```bash python3 scripts/assumption_mapper.py assumptions.csv ``` 4. Validate the problem - Conduct interviews and behavior analysis. - Confirm frequency, severity, and willingness to solve. - Reject weak opportunities early. 5. Validate the solution - Prototype before building. - Run concept, usability, and value tests. - Measure behavior, not only stated preference. 6. Plan discovery sprint - 1-2 week cycle with explicit hypotheses - Daily evidence reviews - End with decision: proceed, pivot, or stop ## Opportunity Solution Tree (Teresa Torres) Structure: - Outcome: metric you want to move - Opportunities: unmet customer needs/pains - Solutions: candidate interventions - Experiments: fastest learning actions Quality checks: - At least 3 distinct opportunities before converging. - At least 2 experiments per top opportunity. - Tie every branch to evidence source. ## Assumption Mapping Assumption categories: - Desirability: users want this - Viability: business value exists - Feasibility: team can build/operate it - Usability: users can successfully use it Prioritization rule: - High risk + low certainty assumptions are tested first. ## Problem Val...
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