Best practices for using Claude Code effectively based on insights from its creator Boris Cherny. Trigger this skill when users ask about optimizing Claude Code usage, configuring CLAUDE.md files, using plan mode, working with sub-agents, understanding Claude Code philosophy, improving coding productivity with Claude Code, or building AI coding tools. Also trigger when users mention blatant demand, scaffolding in AI products, building for future model capabilities, or ask about Anthropic's approach to AI coding assistants.
Y Combinator startup methodology covering team formation, MVP development, growth strategies, fundraising, PR, operations, and hiring. Trigger when users ask about starting a startup, forming a founding team, building an MVP, achieving product-market fit, raising venture capital, startup fundraising strategy, doing PR for startups, startup hiring decisions, startup operations, or when they need guidance on early-stage company building. Also trigger when users mention YC, Y Combinator, startup acceleration, or reference startup fundamentals like runway, burn rate, or co-founder dynamics.
Provides François Chollet's framework for understanding intelligence, AGI development paths, and the limitations of current AI approaches. Use this skill when users ask about- (1) What intelligence really means and how to define AGI, (2) Why scaling pre-training alone won't achieve AGI, (3) The difference between memorized skills and fluid intelligence, (4) Test-time adaptation and its role in AGI, (5) The ARC benchmark and what it measures, (6) Type 1 vs Type 2 abstraction in AI systems, (7) Program synthesis approaches to intelligence, (8) Evaluating claims about AGI progress, or (9) Understanding the conceptual foundations needed for building generally intelligent systems.
Guide for building successful AI startups based on Jake Heller's Casetext journey ($650M exit). Use when users need help with- (1) Selecting AI startup ideas by identifying jobs people pay humans to do, (2) Building reliable AI products through systematic evaluation and prompt iteration, (3) Pricing AI products based on value delivered, (4) Marketing AI products through product quality rather than sales tactics, (5) Understanding the assistance/replacement/unthinkable framework for AI opportunities, (6) Creating evaluation frameworks for AI prompts, or (7) Bridging the trust gap with enterprise customers for AI products.
Strategic framework for evaluating and building B2B AI startups based on Aaron Levie's insights from building Box through the cloud transformation. Use when founders or advisors need to - (1) Evaluate AI startup ideas for defensibility and market timing, (2) Design pricing models for AI products (consumption vs seat-based), (3) Analyze competitive positioning against incumbents, (4) Identify high-value AI opportunities in enterprise unstructured data, (5) Assess whether to target "core" vs "context" business functions, (6) Understand the 2024-2027 AI startup window dynamics, or (7) Apply Innovator's Dilemma and Crossing the Chasm frameworks to AI market entry.
Strategic guidance for building developer tools and AI-first products, derived from Michael Truell's experience building Cursor. Use when- (1) Evaluating whether to enter a market with established competitors, (2) Deciding between product improvement vs growth engineering investment, (3) Architecting AI-assisted developer tools, (4) Choosing between building custom infrastructure vs using existing solutions, (5) Navigating early user feedback that conflicts with product vision, (6) Assessing startup opportunities in AI/developer tools space, (7) Planning technical product launches and distribution strategies.
Explains Andrej Karpathy's framework for understanding the three paradigms of software (1.0- traditional code, 2.0- neural network weights, 3.0- LLM prompts). Use when users ask about software paradigm shifts, the evolution of programming, how LLMs fit into software development history, Software 1.0/2.0/3.0 distinctions, prompt engineering as programming, or when they need to explain or apply Karpathy's mental model for understanding modern AI development. Also useful when discussing how to think about building software in the AI era, choosing between traditional code vs neural nets vs LLM prompts, or explaining the significance of "programming in English."
Apply Andrew Ng's startup building principles and AI-accelerated development strategies from AI Fund's experience launching ~1 startup per month. Use when users ask about startup execution speed, AI coding tools for faster prototyping, agentic AI workflows, evaluating AI startup opportunities, or building AI applications. Triggers include questions about how to build startups faster, AI technology stack layers, where AI opportunities exist, implementing agentic workflows, or applying lessons from successful AI venture studios.