{"id":"8b3637c5-a50f-4488-97e9-2a1f0071ab93","shortId":"J3M8MD","kind":"skill","title":"pm-case-study","tagline":"Generate end-to-end PM case studies from real AI product launches, pivots, and decisions. Analyzes what happened, why, what the PM likely decided, trade-offs made, and lessons learned.","description":"# PM Case Study Skill\n\nGenerate a detailed PM case study from a real AI product launch, pivot, or strategic decision — reconstructing the PM thinking behind it.\n\n## When to Use\n- User asks \"Write a case study on [AI product launch/decision]\"\n- User wants to understand PM decisions behind a real product\n- User says `/pm-case-study` followed by a topic\n- Great for: ChatGPT launch, Claude's Constitutional AI, Gemini's multimodal strategy, GitHub Copilot pricing, Perplexity's search bet, Midjourney's Discord-first strategy, etc.\n\n## Framework: PM Case Study (8 Sections)\n\n### Section 1: Executive Summary\n- **What happened**: One paragraph summary of the product decision/launch\n- **When**: Timeline of key events\n- **Who**: Key people and teams involved\n- **Outcome**: How it played out (success, failure, mixed)\n\n### Section 2: Context & Background\n- **Company situation**: Where was the company at this point? Stage, funding, competitive position.\n- **Market context**: What was happening in the broader market?\n- **Technical context**: What capabilities existed? What was newly possible?\n- **User context**: What were users doing before this product? What pain existed?\n\n### Section 3: The Decision\n- **What was decided**: Specific product/strategy decision\n- **Alternatives considered**: What other paths were likely on the table?\n- **Key trade-offs**: What did they give up by choosing this path?\n- **Stakeholder dynamics**: Who likely championed this? Who likely opposed it?\n\n### Section 4: Execution Analysis\n- **Go-to-market strategy**: How was it launched? Distribution channel?\n- **Phasing**: Was it a big bang launch or phased rollout?\n- **Pricing**: How was it priced? Why that model?\n- **Technical execution**: What was the technical approach? Shortcuts taken?\n\n### Section 5: What Went Right\n- Identify 3-5 specific decisions that contributed to success\n- For each: What was the decision, why it mattered, what would have happened otherwise\n- Be specific — reference actual features, timelines, or metrics where available\n\n### Section 6: What Went Wrong (or Could Have Been Better)\n- Identify 2-3 mistakes, misses, or areas for improvement\n- For each: What happened, what the impact was, what could have been done differently\n- Be fair — hindsight bias is easy, focus on what was knowable at the time\n\n### Section 7: Metrics & Outcomes\n- **Growth metrics**: Users, revenue, market share (use real numbers where available)\n- **Product metrics**: Engagement, retention, satisfaction\n- **Strategic outcomes**: Market position, competitive response, ecosystem effects\n- **Unexpected outcomes**: Things that happened that nobody predicted\n\n### Section 8: Key Takeaways\nExtract 3-5 lessons for product managers:\n- **Lesson**: Clear statement of the principle\n- **Application**: How to apply this in product sense/strategy decisions\n- **Example question**: A product question where this lesson is directly relevant\n\n## Case Study Categories\n\n### Product Launches\n- ChatGPT's launch (Nov 2022) — fastest growing consumer app ever\n- Claude's positioning as the \"safe\" alternative\n- Perplexity's answer engine vs. Google Search\n- Midjourney's Discord-native strategy\n- Cursor's bet on AI-native IDE\n\n### Strategic Pivots\n- An AI lab's shift from nonprofit to capped-profit\n- A safety lab's pivot from pure research to product company\n- A big tech company's emergency response to ChatGPT\n- An open-source LLM strategy from a major tech company\n\n### Feature Decisions\n- ChatGPT Plugins → GPTs → the pivot to actions/agents\n- GitHub Copilot's pricing model ($10/month individual)\n- Claude's Artifacts feature\n- Gemini's multimodal-first approach\n- NotebookLM's audio overview feature\n\n### Pricing & Business Model\n- LLM API pricing evolution (the race to the bottom)\n- ChatGPT Plus ($20/month) → Team → Enterprise tiers\n- The free tier strategy across AI companies\n- Usage-based vs. seat-based pricing in AI\n\n## Output Format\nWrite as a business school case study — structured, analytical, and with clear takeaways. Use real data where available, clearly mark estimates or speculation. Aim for ~2500 words.\n\n## Research-First Workflow (CRITICAL)\nThis skill requires real data:\n1. **Research extensively** — Do 10-15 web searches for: launch details, user growth data, pricing history, company blog posts, founder interviews, analyst reports, and competitor responses.\n2. **Cite everything** — Include `[linked source](url)` inline for all factual claims.\n3. **Date awareness** — Note what was known at the time of the decision vs. what we know now.\n4. **Display** the complete case study.\n\n## What Good Looks Like\n- Demonstrates deep knowledge of the AI product landscape\n- Shows you can analyze real product decisions with nuance\n- Provides concrete examples and data points for product discussions\n- Builds pattern recognition across multiple product launches\n- Reveals your product judgment when you evaluate 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