product-discovery

Solid

Product discovery and market research expert. Use when validating product ideas, conducting market research, user interviews, competitive analysis, or opportunity assessment. Covers JTBD, Kano model, and Value Proposition Canvas.

AI & Automation 145 stars 16 forks Updated today MIT

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Skill Content

# Product Discovery ## Core Principles - **Continuous Discovery** — Weekly user conversations, not episodic research - **Outcome-Driven** — Start with outcomes to achieve, not solutions to build - **Assumption Testing** — Validate risky assumptions before committing resources - **Co-Creation** — Build with customers, not just for them - **Data-Driven** — Use evidence over intuition and stakeholder opinions - **Problem-First** — Deeply understand the problem space before ideating solutions --- ## Hard Rules (Must Follow) > These rules are mandatory. Violating them means the skill is not working correctly. ### No Solution-First Thinking **Never start with a solution. Always define the problem and outcome first.** ```markdown ❌ FORBIDDEN: "We should build a search bar for the product page" "Let's add AI recommendations" "Users need a mobile app" ✅ REQUIRED: "Problem: Users can't find products (40% exit rate on catalog) Outcome: Reduce exit rate to 20% Possible solutions: 1. Search bar with filters 2. AI-powered recommendations 3. Better category navigation 4. Visual product browsing" ``` ### Evidence-Based Decisions **Never assume user needs without evidence from real user research.** ```markdown ❌ FORBIDDEN: - "Users probably want X" (assumption without data) - "Our competitor has X, so we need it too" (copycat without validation) - "The CEO thinks we should build X" (HiPPO without evidence) - "It's obvious users need X" (intuition without validation) ✅ REQUIRED: -...

Details

Author
majiayu000
Repository
majiayu000/claude-arsenal
Created
5 months ago
Last Updated
today
Language
Python
License
MIT

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