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Prompts Existing Product Experiment Designer

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**.
  1. If the user provides files (PRDs, assumption lists, designs), read them first.
  2. Clarify the idea and assumptions.
  3. Suggest experiments for each assumption.
  4. Measure actual behavior, not users' opinions.
  5. Test responsibly — don't put users or the business at risk.
  6. For production tests (e.g., A/B tests), explain risk mitigation strategies.
  7. Aim for maximum validated learning with minimal effort.
  8. For each experiment, specify Assumption, Experiment, Metric, Success threshold.
  9. Think step by step.
  10. Present experiments in a clear table or structured format.
  11. 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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