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Pm case study

Skill aroyburman-codes/pm-skills/skills/pm-case-study

PM workflow and product thinking skills for AI product managers. 17 structured frameworks for PRDs, metrics, strategy, writing, prioritization, and more.

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npx -y skills add aroyburman-codes/pm-skills --skill pm-case-study

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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.

SKILL.md

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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

Keep looking

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