brainstorm-experiments-existing
Design experiments to test assumptions for an existing product — prototypes, A/B tests, spikes, and other low-effort validation methods. Use when validating assumptions, testing feature ideas cheaply, or planning product experiments.
适合你,如果正在为现有产品设计验证实验
npx oh-my-skill add phuryn/pm-skills/brainstorm-experiments-existingcurl -fsSL https://oh-my-skill.com/install.sh | bash -s -- phuryn/pm-skills/brainstorm-experiments-existingnpx oh-my-skill verify phuryn/pm-skills/brainstorm-experiments-existing怎么用
商店整理自技能原文 · 版本 18468a9 · 表述以原文为准当用户描述一个现有产品的功能想法和假设时,Claude 会设计低成本的实验来验证这些假设,例如原型测试、A/B 测试、技术探针等,并给出每个实验的假设、具体做法、衡量指标和成功标准。
当用户需要验证现有产品的某个功能假设,或想低成本测试新想法时触发。用户需提供想法和假设,也可附上 PRD 或假设列表等文件。
技能原文 SKILL.md
Design Experiments (Existing Product)
Design low-effort experiments to test product assumptions before committing to full implementation.
Context
You are helping a product team design experiments for $ARGUMENTS. The team has a feature idea and assumptions that need validation.
If the user provides files (PRDs, assumption lists, designs), read them first.
Instructions
The user will describe their idea and assumptions. Work through these steps:
- Clarify the idea and assumptions: Confirm what the team wants to build and what they need to validate.
- Suggest experiments for each assumption. Consider methods like:
- First-click testing or task completion with a prototype
- Feature stubs or fake door tests
- Technical spikes
- A/B tests on production (with risk mitigation)
- Wizard of Oz approaches
- Survey-based validation (behavioral, not opinion-based)
- Key principles to follow:
- Measure actual behavior, not users' opinions
- Test responsibly — don't put users or the business at risk
- For production tests (e.g., A/B tests), explain risk mitigation strategies
- Aim for maximum validated learning with minimal effort
- For each experiment, specify:
- Assumption: What do we believe?
- Experiment: What exactly will we do to validate it?
- Metric: What will be measured?
- Success threshold: The expected value if we are right
Think step by step. Present experiments in a clear table or structured format. Save as markdown if substantial.