# Pm Case Study > 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. - **Type:** Skill - **Install:** `agentstack add skill-aroyburman-codes-pm-skills-pm-case-study` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [aroyburman-codes](https://agentstack.voostack.com/s/aroyburman-codes) - **Installs:** 0 - **Category:** [Data & Analytics](https://agentstack.voostack.com/c/data-and-analytics) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [aroyburman-codes](https://github.com/aroyburman-codes) - **Source:** https://github.com/aroyburman-codes/pm-skills/tree/main/skills/pm-case-study ## Install ```sh agentstack add skill-aroyburman-codes-pm-skills-pm-case-study ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # PM Case Study Skill Generate a detailed PM case study from a real AI product launch, pivot, or strategic decision — reconstructing the PM thinking behind it. ## When to Use - User asks "Write a case study on [AI product launch/decision]" - User wants to understand PM decisions behind a real product - User says `/pm-case-study` followed by a topic - 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. ## Framework: PM Case Study (8 Sections) ### Section 1: Executive Summary - **What happened**: One paragraph summary of the product decision/launch - **When**: Timeline of key events - **Who**: Key people and teams involved - **Outcome**: How it played out (success, failure, mixed) ### Section 2: Context & Background - **Company situation**: Where was the company at this point? Stage, funding, competitive position. - **Market context**: What was happening in the broader market? - **Technical context**: What capabilities existed? What was newly possible? - **User context**: What were users doing before this product? What pain existed? ### Section 3: The Decision - **What was decided**: Specific product/strategy decision - **Alternatives considered**: What other paths were likely on the table? - **Key trade-offs**: What did they give up by choosing this path? - **Stakeholder dynamics**: Who likely championed this? Who likely opposed it? ### Section 4: Execution Analysis - **Go-to-market strategy**: How was it launched? Distribution channel? - **Phasing**: Was it a big bang launch or phased rollout? - **Pricing**: How was it priced? Why that model? - **Technical execution**: What was the technical approach? Shortcuts taken? ### Section 5: What Went Right - Identify 3-5 specific decisions that contributed to success - For each: What was the decision, why it mattered, what would have happened otherwise - Be specific — reference actual features, timelines, or metrics where available ### Section 6: What Went Wrong (or Could Have Been Better) - Identify 2-3 mistakes, misses, or areas for improvement - For each: What happened, what the impact was, what could have been done differently - Be fair — hindsight bias is easy, focus on what was knowable at the time ### Section 7: Metrics & Outcomes - **Growth metrics**: Users, revenue, market share (use real numbers where available) - **Product metrics**: Engagement, retention, satisfaction - **Strategic outcomes**: Market position, competitive response, ecosystem effects - **Unexpected outcomes**: Things that happened that nobody predicted ### Section 8: Key Takeaways Extract 3-5 lessons for product managers: - **Lesson**: Clear statement of the principle - **Application**: How to apply this in product sense/strategy decisions - **Example question**: A product question where this lesson is directly relevant ## Case Study Categories ### Product Launches - ChatGPT's launch (Nov 2022) — fastest growing consumer app ever - Claude's positioning as the "safe" alternative - Perplexity's answer engine vs. Google Search - Midjourney's Discord-native strategy - Cursor's bet on AI-native IDE ### Strategic Pivots - An AI lab's shift from nonprofit to capped-profit - A safety lab's pivot from pure research to product company - A big tech company's emergency response to ChatGPT - An open-source LLM strategy from a major tech company ### Feature Decisions - ChatGPT Plugins → GPTs → the pivot to actions/agents - GitHub Copilot's pricing model ($10/month individual) - Claude's Artifacts feature - Gemini's multimodal-first approach - NotebookLM's audio overview feature ### Pricing & Business Model - LLM API pricing evolution (the race to the bottom) - ChatGPT Plus ($20/month) → Team → Enterprise tiers - The free tier strategy across AI companies - Usage-based vs. seat-based pricing in AI ## Output Format Write 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. ## Research-First Workflow (CRITICAL) This skill requires real data: 1. **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. 2. **Cite everything** — Include `[linked source](url)` inline for all factual claims. 3. **Date awareness** — Note what was known at the time of the decision vs. what we know now. 4. **Display** the complete case study. ## What Good Looks Like - Demonstrates deep knowledge of the AI product landscape - Shows you can analyze real product decisions with nuance - Provides concrete examples and data points for product discussions - Builds pattern recognition across multiple product launches - Reveals your product judgment when you evaluate decisions ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [aroyburman-codes](https://github.com/aroyburman-codes) - **Source:** [aroyburman-codes/pm-skills](https://github.com/aroyburman-codes/pm-skills) - **License:** MIT Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-aroyburman-codes-pm-skills-pm-case-study - Seller: https://agentstack.voostack.com/s/aroyburman-codes - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.