# Experiment design planner Human Guide

## What This Is For
Designs fast, reliable validation experiments with hypothesis, method, success metric, and decision rules (Ship/Iterate/Kill). It gives the agent a clearer input/output frame for experiment design planner: what context to ask for, what decisions to make, and what usable artifact to return.

Use this as a human-readable version of the Experiment design planner agent skill. It is meant for marketers, operators, founders, and other non-coders who want the workflow without reading agent-specific implementation instructions.

## When To Use This
- Use this when you need a repeatable process for experiment design planner.
- Use this when the task needs judgment, examples, constraints, or a clear output format rather than a one-off prompt.
- Use this when you want to hand an AI assistant enough context to produce a usable marketing artifact.

## When Not To Use This
- Do not use this when you only need a quick factual answer.
- Do not use this when the work depends on private data you cannot share with the assistant.
- Do not use this as a replacement for legal, compliance, financial, or medical review.

## What You Need Before Starting
- The goal or business outcome you want.
- The audience, customer segment, or market context.
- Any source material the assistant should respect, such as notes, briefs, examples, URLs, or brand guidance.
- Constraints such as tone, length, channel, deadline, region, or approval requirements.
- A clear definition of what a good final answer should look like.

## Step-By-Step Workflow
1. State the job clearly: "Use the Experiment design planner guide to help me with..."
2. Add context: audience, goal, offer, channel, source material, and constraints.
3. Ask the assistant to identify missing inputs before producing the final output.
4. Have the assistant follow the skill-specific guidance below.
5. Review the result against the final checklist and ask for revisions where needed.

## Skill-Specific Guidance
- Choose the smallest valid test.
- Set sample size or duration.

## Decision Points And Nuance
The original skill emphasizes: Purpose, Steps, Output, Hypothesis, Experiment Method, Success Metric, Duration / Sample Size, Decision Rule, References, Supporting file: references/example.md.

Use these questions to steer the work:
- What is the intended audience or buyer?
- What source material must be preserved?
- What should the assistant optimize for: clarity, persuasion, accuracy, speed, creativity, or conversion?
- What examples represent the desired quality bar?
- What should the assistant avoid?

## Common Mistakes
- Starting without a clear audience or goal.
- Asking for a final artifact before sharing examples or constraints.
- Accepting a generic first draft without checking it against the intended use.

## Copy-And-Paste Prompt
```text
Use the Experiment design planner human guide.

My goal:
[Describe the business outcome]

Audience:
[Describe who this is for]

Context and source material:
[Paste notes, examples, links, or existing copy]

Constraints:
[Tone, length, channel, timeline, must-include items, must-avoid items]

Before producing the final output, ask me for any missing information that would materially improve the result.
```

## Final Checklist
- [ ] The output matches the original goal.
- [ ] The audience and context are reflected in the answer.
- [ ] Important constraints and source material were preserved.
- [ ] The assistant made the relevant decisions explicit.
- [ ] The final artifact is ready to use, review, or hand to the next person.

## Source
This guide was generated from the pratikshadake/claude-product-management-skills skill entry for `experiment-design`.

## Source Skill Notes
These notes preserve the nuance from the original skill. Use them as supporting reference when the workflow above feels too generic.

# Experiment Design Planner

## Purpose
Design fast, reliable validation experiments.

## Steps
1. State the hypothesis.
2. Choose the smallest valid test.
3. Define success metric.
4. Set sample size or duration.
5. Define decision rule.

## Output

### Hypothesis
### Experiment Method
### Success Metric
### Duration / Sample Size
### Decision Rule
Ship / Iterate / Kill

## References
See [worked example](references/example.md) for a complete scenario.

---

## Supporting file: references/example.md

# Example: Experiment Design — AI Meeting Notes Pricing

## Hypothesis
Founders will pay $10/month for automatic summaries.

## Experiment Method
Landing page with pricing + waitlist.

## Success Metric
≥ 5% visitor-to-signup conversion.

## Duration / Sample Size
1,000 visitors or 7 days.

## Decision Rule
- ≥ 5% → proceed  
- 2–5% → iterate pricing  
- < 2% → reconsider value