# How to Design Experiments to Test Assumptions Using the PM Skills Marketplace

> Design experiments to test assumptions with the PM Skills Marketplace. Clarify assumptions, define metrics, and create structured experiment plans using specialized skills.

- Repository: [Pawel Huryn/pm-skills](https://github.com/phuryn/pm-skills)
- Tags: how-to-guide
- Published: 2026-06-15

---

**The PM Skills Marketplace provides two specialized skills—`brainstorm-experiments-existing` and `brainstorm-experiments-new`—that guide you through clarifying assumptions, defining metrics, and outputting structured experiment plans with success thresholds.**

The **PM Skills Marketplace** is a collection of Markdown-defined frameworks hosted in the `phuryn/pm-skills` repository. When you need to validate product hypotheses without wasting engineering resources, these deterministic skills orchestrate Claude-powered assistants through a rigorous experiment design process.

## Understanding the Experiment Design Skills

The marketplace offers two complementary skills located in the `pm-product-discovery` plugin. Each skill follows a deterministic, step-by-step process defined in its respective [`SKILL.md`](https://github.com/phuryn/pm-skills/blob/main/SKILL.md) file.

### Brainstorm Experiments for Existing Products

The **`brainstorm-experiments-existing`** skill, defined in [[`pm-product-discovery/skills/brainstorm-experiments-existing/SKILL.md`](https://github.com/phuryn/pm-skills/blob/main/pm-product-discovery/skills/brainstorm-experiments-existing/SKILL.md)](https://github.com/phuryn/pm-skills/blob/main/pm-product-discovery/skills/brainstorm-experiments-existing/SKILL.md), designs low-effort experiments for live products. It guides you through idea clarification, assumption identification, and suggests concrete tests like first-click prototypes or fake-door experiments.

### Brainstorm Experiments for New Products

The **`brainstorm-experiments-new`** skill, located at [[`pm-product-discovery/skills/brainstorm-experiments-new/SKILL.md`](https://github.com/phuryn/pm-skills/blob/main/pm-product-discovery/skills/brainstorm-experiments-new/SKILL.md)](https://github.com/phuryn/pm-skills/blob/main/pm-product-discovery/skills/brainstorm-experiments-new/SKILL.md), focuses on lean-startup pretotype experiments. It forces you to create an **XYZ hypothesis** (e.g., "X% of Y will do Z") and suggests validation methods like landing pages, explainer videos, or pre-order tests that require "skin-in-the-game" from potential customers.

## The Experiment Design Workflow

Both skills accept an **argument string** (`$ARGUMENTS`) describing your product context, assumptions, and file attachments. The execution follows a strict five-step process:

1. **Read user-provided files** (PRDs, analytics CSVs, or data dumps) automatically loaded by the assistant.
2. **Clarify the idea** (existing) or **create an XYZ hypothesis** (new) to frame the core assumption.
3. **Suggest 2-3 concrete experiments** targeting behavioral validation rather than opinion surveys.
4. **Specify for each experiment**:
   - *Assumption* being tested
   - *Experiment* description
   - *Metric* to capture (behavioral, not attitudinal)
   - *Success threshold* (e.g., "≥ 15% sign-ups")
5. **Output a markdown table** ready for direct insertion into your PRD or team wiki.

## Running Experiments Through Commands and Skills

You can invoke these skills through multiple interfaces depending on your environment.

### Using Claude Code CLI

Install the discovery plugin once, then invoke the skill via natural language:

```bash

# Install the plugin

claude plugin install pm-product-discovery@pm-skills

# Request experiment design for an existing product

claude chat "Design experiments for the following assumptions:
- Reducing onboarding churn by improving the welcome flow
- Increasing daily active users by adding a social share button"

```

The assistant automatically loads `brainstorm-experiments-existing` and returns a structured markdown table with metrics and success thresholds.

### Using Slash Commands in Claude Cowork

In Claude Cowork environments, use explicit slash commands to trigger the skill:

```

/brainstorm experiments existing — We need to reduce churn in our onboarding flow

```

This command triggers the same skill, reads any attached files (e.g., an onboarding analytics CSV), and produces a structured experiment plan.

### Chaining the Full Discovery Flow

The **`/discover`** command, defined in [[`pm-product-discovery/commands/discover.md`](https://github.com/phuryn/pm-skills/blob/main/pm-product-discovery/commands/discover.md)](https://github.com/phuryn/pm-skills/blob/main/pm-product-discovery/commands/discover.md), orchestrates a complete discovery workflow by chaining multiple skills:

1. `brainstorm-ideas-new`
2. `identify-assumptions-new`
3. `prioritize-assumptions`
4. `brainstorm-experiments-new`

Invoke it with a single product concept:

```

/discover AI-powered meeting summarizer for remote teams

```

The final output includes both a prioritized assumption matrix and a set of lean-startup experiments targeting the highest-risk assumptions.

### Direct Integration with OpenCode

For programmatic access, call the skill directly from Python scripts:

```python
from opencode import Claude

assistant = Claude()
response = assistant.ask("""
Design experiments for the following assumptions (existing product):
1. Users abandon the checkout after entering payment details.
2. Adding a progress bar will increase completion rates.
""")
print(response)   # Markdown table with experiments, metrics, thresholds

```

## Prioritizing Assumptions Before Experimentation

Before designing experiments, use the **`prioritize-assumptions`** skill located at [[`pm-product-discovery/skills/prioritize-assumptions/SKILL.md`](https://github.com/phuryn/pm-skills/blob/main/pm-product-discovery/skills/prioritize-assumptions/SKILL.md)](https://github.com/phuryn/pm-skills/blob/main/pm-product-discovery/skills/prioritize-assumptions/SKILL.md). This framework scores assumptions on an **Impact × Risk** matrix and automatically tags high-impact/high-risk items that require immediate experimentation.

Running this skill first ensures you only design experiments for assumptions that actually matter, avoiding wasted effort on low-risk optimizations.

## Summary

- The **PM Skills Marketplace** provides **`brainstorm-experiments-existing`** and **`brainstorm-experiments-new`** skills to validate assumptions for live and hypothetical products.
- Each skill accepts an argument string (`$ARGUMENTS`) and outputs a markdown table containing assumptions, metrics, and success thresholds.
- The **`/discover`** command chains the full workflow from ideation to experiment design.
- Skills are plain Markdown files that work across Claude Code, Claude Cowork, OpenCode, and Gemini CLI.
- Always run **`prioritize-assumptions`** first to target high-risk items.

## Frequently Asked Questions

### What is the PM Skills Marketplace?

The **PM Skills Marketplace** is a repository of Markdown-defined skills and commands hosted at `phuryn/pm-skills`. It provides self-contained frameworks that Claude-powered assistants can invoke to perform structured product management tasks, including experiment design, assumption prioritization, and PRD generation.

### How do I choose between existing and new product experiment skills?

Use **`brainstorm-experiments-existing`** when your product is already in the market and you need to optimize specific metrics like churn or conversion. Use **`brainstorm-experiments-new`** when validating a pre-launch concept or pivot, as it forces XYZ hypothesis creation and lean-startup pretotypes like landing pages or pre-orders.

### Can I use these skills outside of Claude?

Yes. Because the skills are plain Markdown files, they function in any assistant environment that can read [`SKILL.md`](https://github.com/phuryn/pm-skills/blob/main/SKILL.md) files and render the output, including OpenCode, Gemini CLI, and custom implementations. The only requirement is that the underlying LLM can process the structured instructions contained in the files.

### What makes a good experiment metric in this framework?

The framework requires **behavioral metrics** over attitudinal ones. Instead of asking "Would you use this?" (opinion), the skills force you to measure actions like sign-up rates, button clicks, or pre-order completions. Each experiment must define a specific success threshold (e.g., "≥ 15% click-through rate") before execution begins.