product manager product skill risk: low
Existing Product Experiment Designer
Guides design of low-effort experiments to test assumptions for an existing product feature idea, following steps to clarify assumptions, suggest methods such as prototypes, A/B te…
SKILL 1 file
SKILL.md
--- name: brainstorm-experiments-existing description: "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." --- ## 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: 1. **Clarify the idea and assumptions**: Confirm what the team wants to build and what they need to validate. 2. **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) 3. **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 4. **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. --- ### Further Reading - [Testing Product Ideas: The Ultimate Validation Experiments Library](https://www.productcompass.pm/p/the-ultimate-experiments-library) - [Assumption Prioritization Canvas: How to Identify And Test The Right Assumptions](https://www.productcompass.pm/p/assumption-prioritization-canvas) - [What Is Product Discovery? The Ultimate Guide Step-by-Step](https://www.productcompass.pm/p/what-exactly-is-product-discovery) - [Continuous Product Discovery Masterclass (CPDM)](https://www.productcompass.pm/p/cpdm) (video course)
INPUTS
- $ARGUMENTS REQUIRED
the product or feature being discussed
REQUIRED CONTEXT
- feature idea and assumptions to validate
- optionally PRDs, assumption lists, or designs
ROLES & RULES
Role assignments
- You are helping a product team design experiments for **$ARGUMENTS**.
- If the user provides files (PRDs, assumption lists, designs), read them first.
- Clarify the idea and assumptions.
- Suggest experiments for each assumption.
- 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, Experiment, Metric, Success threshold.
- Think step by step.
- Present experiments in a clear table or structured format.
- Save as markdown if substantial.
EXPECTED OUTPUT
- Format
- markdown
- Schema
- table · Assumption, Experiment, Metric, Success threshold
- Constraints
- present experiments in a clear table or structured format
- for each experiment specify assumption, experiment, metric, and success threshold
- save as markdown if substantial
SUCCESS CRITERIA
- Design low-effort experiments to test product assumptions before committing to full implementation.
- Confirm what the team wants to build and what they need to validate.
- Suggest experiments using listed methods.
- Measure actual behavior, not users' opinions.
- Test responsibly.
- Aim for maximum validated learning with minimal effort.
CAVEATS
- Dependencies
- User-provided files (PRDs, assumption lists, designs) if present.
- Ambiguities
- $ARGUMENTS placeholder is referenced but not explained in usage
- 'Save as markdown if substantial' does not define substantial
- Exact output format (table vs structured) is not specified
QUALITY
- OVERALL
- 0.82
- CLARITY
- 0.85
- SPECIFICITY
- 0.80
- REUSABILITY
- 0.90
- COMPLETENESS
- 0.75
IMPROVEMENT SUGGESTIONS
- Add explicit instruction for output format (e.g., always use markdown table with defined columns)
- Clarify how to handle provided files (e.g., 'Summarize relevant sections before proceeding')
USAGE
Copy the prompt above and paste it into your AI of choice — Claude, ChatGPT, Gemini, or anywhere else you're working. Replace any placeholder sections with your own context, then ask for the output.
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