brainstorm-experiments-new
This skill generates quantified XYZ hypotheses and designs minimal-effort pretotype experiments to validate new product concepts using lean startup methodology. Use it when testing whether a target market will actually engage with a product idea, creating experiments like landing pages, explainer videos, or pre-order campaigns that measure real behavior rather than stated interest.
git clone --depth 1 https://github.com/phuryn/pm-skills /tmp/brainstorm-experiments-new && cp -r /tmp/brainstorm-experiments-new/pm-product-discovery/skills/brainstorm-experiments-new ~/.claude/skills/brainstorm-experiments-newSKILL.md
## Design Lean Startup Experiments (New Product) Create XYZ hypotheses and design pretotype experiments to validate a new product concept with minimal effort. ### Context You are helping validate a new product concept: **$ARGUMENTS** using lean startup methodology. If the user provides files (market research, landing page mockups), read them first. ### Instructions 1. **Create an XYZ Hypothesis** in the form: "At least X% of Y will do Z" - **X%**: The percentage of the target market expected to engage - **Y**: The specific target market (e.g., "mid-size luxury sedan buyers") - **Z**: How they will engage with the product 2. **Suggest 2-3 pretotype experiments** to test the hypothesis with minimal effort. Consider: - **Landing Page**: Test interest by measuring sign-ups or clicks - **Explainer Video**: Test understanding and appeal through engagement metrics - **Email Campaign**: Test demand through response and click-through rates - **Pre-Order / Waitlist**: Test willingness to pay through skin-in-the-game commitment - **Concierge / Manual MVP**: Deliver the service manually to test value 3. **Key principles** (Alberto Savoia, *The Right It*): - **Skin-in-the-Game**: Test willingness to pay — not just interest. Real commitment (time, money, reputation) is the only reliable signal. - **Your Own Data (YODA)**: Collect your own data through experiments rather than relying on Others' Data (ODP) like market reports or analogies. "The market for your idea does not care about the market for someone else's idea." - Measure actual behavior, not users' opinions 4. **For each experiment**, specify the hypothesis being tested, the method, the metric, and the success threshold. Think step by step. Save as markdown if substantial. --- ### Further Reading - [How to Build the Right Product with Alberto Savoia (ex-Innovator at Google)](https://www.productcompass.pm/p/how-to-build-the-right-product-with) - [Testing Product Ideas: The Ultimate Validation Experiments Library](https://www.productcompass.pm/p/the-ultimate-experiments-library) - [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.
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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.