{"id":"c7443450-0ea2-4fb3-ac7c-0c19f028fd30","shortId":"bapqGu","kind":"skill","title":"ai-product-teardown","tagline":"Structured teardown of AI products (ChatGPT, Claude, Gemini, Perplexity, Copilot, etc.). Analyzes product decisions, UX patterns, technical architecture, business model, and competitive positioning.","description":"# AI Product Teardown Skill\n\nPerform a structured, opinionated teardown of any AI product — analyzing the product decisions, UX, technical architecture, business model, and competitive positioning from a PM lens.\n\n## When to Use\n- User asks \"Tear down [AI product]\" or \"Analyze [AI product]\"\n- User wants to understand the product thinking behind an AI feature\n- User wants to build product intuition about AI products\n- User says `/ai-product-teardown` followed by a product name\n- Great for: ChatGPT, Claude, Gemini, Perplexity, Copilot, Midjourney, Cursor, v0, NotebookLM, etc.\n\n## Framework: AI Product Teardown (7 Sections)\n\n### Section 1: Product Overview\n- **What it is**: One-sentence description\n- **Company**: Who built it, their mission, and strategic context\n- **Launch date & trajectory**: When launched, key milestones, current scale\n- **Target users**: Primary and secondary audiences\n- **Business model**: How it makes money (or plans to)\n\n### Section 2: Core Value Proposition\n- **Job to be Done**: What fundamental job does this product do for users?\n- **10x moment**: What's the moment where users think \"this is magic\"?\n- **Switching cost**: What would it take to switch away?\n- **Network effects**: Does it get better with more users? How?\n\n### Section 3: UX & Product Decisions\nWalk through the key product decisions and evaluate each:\n- **Onboarding flow**: How does a new user go from zero to value?\n- **Core interaction model**: Chat? Canvas? Structured output? Multi-modal?\n- **Information architecture**: How is functionality organized?\n- **Personalization**: How does it adapt to different users?\n- **Error handling**: What happens when the AI is wrong?\n\nFor each decision, evaluate:\n- What they got RIGHT and why\n- What they got WRONG or could improve\n- What trade-off they're making (and whether you'd make the same one)\n\n### Section 4: Technical Architecture (PM Lens)\nAnalyze the technical choices from a product perspective:\n- **Model strategy**: Which model(s)? Why that capability level?\n- **Latency vs. quality trade-off**: Where do they sit on the spectrum?\n- **Context & memory**: How does it handle conversation history?\n- **Safety & guardrails**: What's their content policy approach?\n- **Tool use / plugins / integrations**: How extensible is it?\n- **Pricing architecture**: How do technical costs map to pricing?\n\n### Section 5: Growth & Distribution\n- **Acquisition channels**: How do users find this? (organic, viral, paid, partnerships)\n- **Activation**: What gets users to the \"aha moment\"?\n- **Retention loops**: What brings users back?\n- **Monetization**: Free → paid conversion strategy\n- **Viral mechanics**: Does usage naturally create awareness?\n\n### Section 6: Competitive Positioning\n- **Direct competitors**: Who else does this job?\n- **Positioning map**: Plot on 2x2 (e.g., capability vs. safety, consumer vs. enterprise)\n- **Sustainable moats**: What's defensible? (data, distribution, brand, model quality, ecosystem)\n- **Vulnerability**: Where could a competitor win?\n\n### Section 7: PM Recommendations\nIf you were the PM, what would you do next?\n- **Top 3 features to build** (with reasoning and expected impact)\n- **Top 1 thing to kill or change** (what's not working)\n- **Strategic bet**: One big swing that could transform the product\n- **Metrics to watch**: What would you track weekly?\n\n## Output Format\nWrite as an opinionated product review — structured but with a clear point of view. Use screenshots/descriptions of specific UI elements where relevant. Aim for ~2000 words. Be specific and cite real features.\n\n## Research-First Workflow\n1. **Research** — Search for latest product updates, user reviews, competitor announcements, company blog posts, and usage data. Do 5-10 searches.\n2. **Cite sources** — Include `[linked source](url)` inline for factual claims.\n3. **Display** the complete teardown.\n\n## What Good Looks Like\n- Shows you've done homework on the product landscape\n- Demonstrates structured product thinking on real products\n- Reveals your product taste and judgment\n- Provides concrete examples to reference in product discussions\n- Builds intuition about AI product patterns across the 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