brainstorm-experiments-existing
This skill helps product teams design low-effort experiments to validate assumptions about existing product features before full implementation. Use it when you need to test feature ideas cheaply, choose between design approaches, or plan product experiments through methods like prototypes, A/B tests, fake door tests, technical spikes, or behavioral surveys. The skill structures validation work by clarifying assumptions, suggesting appropriate experiment methods, and defining success metrics that focus on measuring actual user behavior rather than opinions.
git clone --depth 1 https://github.com/phuryn/pm-skills /tmp/brainstorm-experiments-existing && cp -r /tmp/brainstorm-experiments-existing/pm-product-discovery/skills/brainstorm-experiments-existing ~/.claude/skills/brainstorm-experiments-existingSKILL.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: 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)
The method for finding the gap between what a system is supposed to do and what the code actually does — the class of bug generic scanners miss because they have no model of intent. Defines what counts as documented intent, what counts as implementation evidence, which mismatches matter, and how to avoid hand-wavy findings. Use when auditing AI-built code, reviewing access control against documented permissions, or checking whether a codebase matches its own documentation.
The durable documentation set that makes an AI-built (vibe-coded) app reviewable before shipping. A small core every app needs — architecture, user/permission flows, permissions, variables/secrets, and a test-coverage map — plus conditional docs added only when they apply: emails, scheduled work, SEO, and embedded agents/automation. Defines what each doc must capture and how a reviewer or auditor uses it. Use when documenting a codebase for handoff, mapping user journeys and trust-boundary crossings, planning test coverage, or preparing for a security or performance audit.
Analyze A/B test results with statistical significance, sample size validation, confidence intervals, and ship/extend/stop recommendations. Use when evaluating experiment results, checking if a test reached significance, interpreting split test data, or deciding whether to ship a variant.
Perform cohort analysis on user engagement data — retention curves, feature adoption trends, and segment-level insights. Use when analyzing user retention by cohort, studying feature adoption over time, investigating churn patterns, or identifying engagement trends.
Generate SQL queries from natural language descriptions. Supports BigQuery, PostgreSQL, MySQL, and other dialects. Reads database schemas from uploaded diagrams or documentation. Use when writing SQL, building data reports, exploring databases, or translating business questions into queries.
Brainstorm team-level OKRs aligned with company objectives — qualitative objectives with measurable key results. Use when setting quarterly OKRs, aligning team goals with company strategy, drafting objectives, or learning how to write effective OKRs.
Create a Product Requirements Document using a comprehensive 8-section template covering problem, objectives, segments, value propositions, solution, and release planning. Use when writing a PRD, documenting product requirements, preparing a feature spec, or reviewing an existing PRD.
Generate realistic dummy datasets for testing with customizable columns, constraints, and output formats (CSV, JSON, SQL, Python script). Use when creating test data, building mock datasets, or generating sample data for development and demos.