pm-frameworks

name	pm-frameworks
description	Expert knowledge of proven product management frameworks for discovery, growth, measurement, planning, and AI-era practices.

PM Frameworks Skill

Expert knowledge of proven product management frameworks for discovery, growth, measurement, planning, and AI-era practices.

When to Invoke

Auto-invoke this skill when users discuss:

• Discovery: Feature validation, user research, testing assumptions, "should we build this?", risk assessment
• Growth: Acquisition, retention, virality, growth loops, product-led growth, network effects
• Planning: Roadmaps, prioritization, now-next-later, LNO framework, scoping projects
• Measurement: PMF surveys, metrics, success criteria, measuring product-market fit
• AI Products: Evals, fine-tuning vs RAG, prompt engineering, AI unit economics, production AI systems
• Strategy: Four fits, market-product fit, competitive positioning, business model
• Execution: PRDs, specs, issues vs stories, prototype-first development

Core Frameworks

Discovery Frameworks

Located in /frameworks/discovery/

Four Risks (Marty Cagan)

• Value Risk: Will customers buy/use this?
• Usability Risk: Can users figure it out?
• Feasibility Risk: Can we build it?
• Business Viability Risk: Does it work for the business?

Continuous Discovery (Teresa Torres)

• Weekly touchpoints with customers
• Opportunity Solution Trees
• Assumption testing
• Small experiments over big bets

Growth Frameworks

Located in /frameworks/growth/

Growth Loops (Elena Verna)

• Viral loops (user invites user)
• Content/SEO loops (content attracts users)
• Network effect loops (more users = more value)
• Paid loops (revenue funds acquisition)

Four Fits (Brian Balfour)

• Market-Product Fit
• Product-Channel Fit
• Channel-Model Fit
• Model-Market Fit

Product-Led Sales

• Self-serve to sales-assist progression
• Usage-based qualification
• Expansion revenue patterns

Planning Frameworks

Located in /frameworks/planning/

Now-Next-Later (Janna Bastow)

• Now: Current sprint, high confidence
• Next: Next 1-3 months, medium confidence
• Later: Future possibilities, low confidence
• Cone of uncertainty principle

LNO Prioritization (Shreyas Doshi)

• Leverage: High impact, do these first
• Neutral: Expected work, batch and schedule
• Overhead: Low value, minimize or eliminate

Scope Projects Down

• 80/20 principle for features
• Minimum viable scope
• Cut ruthlessly, add back later

Measurement Frameworks

Located in /frameworks/measurement/

PMF Survey (Rahul Vohra)

• "How disappointed would you be if you couldn't use this product?"
• Target 40%+ "very disappointed"
• Find your high-expectation customers
• Build for them, ignore the rest

AI-Era Practices

Located in /frameworks/ai-era-practices/

Prototype-First (Aakash Gupta)

• Ship prototypes before docs
• Code is the spec
• Iterate faster than you document

Issues Not Stories (Linear)

• Describe the problem, not the solution
• Let engineers figure out how
• Direction → Building → Quality phases

AI Unit Economics

• Cost per interaction modeling
• Inference costs at scale
• Value vs cost tradeoffs

Continuous Calibration

• Agency vs control spectrum
• When to give AI more autonomy
• Testing probabilistic systems

Organizational AI Adoption (CODER Framework)

• Culture, Organization, Data, Expertise, Roadmap
• Systematic approach to AI transformation

AI Technical Frameworks

Located in /frameworks/ai/

Production AI Systems (Chip Huyen)

• Data quality > model complexity
• Monitoring and observability
• Handling model degradation

Fine-tuning vs RAG Decision

• RAG for dynamic data, domain knowledge
• Fine-tuning for style, task specialization
• Cost and maintenance tradeoffs

AI Evals (Aman Khan)

• Prompt-level testing
• Task-level testing
• System-level testing
• Regression testing for AI

How to Apply Frameworks

Conversationally, Not as Lectures

Don't say: "Let me explain the Four Risks framework..." Do say: "What evidence do you have that users want this? That's the value risk."

Ask Questions That Apply Frameworks

• "What's the smallest thing you could test this week?" (Continuous Discovery)
• "Is this Leverage, Neutral, or Overhead work?" (LNO)
• "Can you prototype this before writing the PRD?" (Prototype-First)
• "What's your growth loop here?" (Growth Loops)

Push Toward Action

• Prototype over document
• Test with users over internal debate
• Small experiments over big bets
• Evidence over opinion

Thought Leaders

Detailed profiles in /thought-leaders/:

• Marty Cagan - Discovery, empowered teams, four risks
• Teresa Torres - Continuous discovery, opportunity trees
• Elena Verna - Growth loops, product-led growth
• Brian Balfour - Four fits, growth strategy
• Chip Huyen - Production AI, ML engineering
• Aman Khan - AI evals, vibe-driven development
• Janna Bastow - Now-Next-Later, roadmapping
• Aakash Gupta - Prototype-first, visual frameworks
• Rahul Vohra - PMF survey, high-expectation customers
• Ravi Mehta - Product Strategy Stack

Integration with Commands

This skill provides background knowledge for:

• /strategy-session - Apply frameworks conversationally
• /four-risks - Deep dive on risk assessment
• /growth-loops - Identify growth mechanisms
• /four-fits - Assess market-product alignment
• /lno-prioritize - Categorize work by leverage
• /now-next-later - Build roadmaps
• /pmf-survey - Measure product-market fit
• /ai-cost-check - Model AI economics
• /start-evals - Design AI evaluation

Key Principles

1. Evidence over opinion - Always ask "what evidence do we have?"
2. Prototype over document - Ship something testable
3. Users over internal debate - Talk to real customers
4. Small experiments - Test assumptions cheaply
5. Frameworks as tools, not rules - Apply judgment

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This skill surfaces PM frameworks from the /frameworks/ and /thought-leaders/ directories.