ycombinator-skills

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.

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.

Strategic guidance on AI scaling laws, capability trajectories, and building products at the frontier of AI capabilities. Use when users ask about AI scaling trends, capability forecasting, planning AI product development timelines, understanding pretraining vs reinforcement learning phases, interpreting AI benchmark improvements, deciding when to build AI products that don't quite work yet, or strategizing around rapidly advancing AI capabilities. Also triggers for questions about task horizon doubling, Jevons paradox in AI, or how to position products for future model improvements.

Guidance for building AI systems for scientific discovery, based on lessons from AlphaFold's development. Use when designing ML systems for scientific domains, planning validation strategies using blind benchmarks, deciding how to release scientific AI tools for maximum impact, evaluating data acquisition vs architectural research investment, building tools for domain expert adoption, structuring ML research projects with compute budgeting, or assessing when a scientific AI tool has crossed the relevance threshold for real-world use.

Knowledge base containing insights from Aravind Srinivas (Perplexity CEO) on building AI-powered search products, competitive strategy against well-funded incumbents, and the future of agentic browsers. Use this skill when users ask about Perplexity's strategy, AI search product development, competing with Google/OpenAI/Anthropic, building answer engines, agentic browser concepts, startup competitive moats, or when analyzing the AI search market landscape. Also use when discussing how to position AI products against incumbents or when exploring the "cognitive operating system" concept for browsers.

Strategic guidance for AI startup founders based on Sam Altman's insights from OpenAI's journey. Use this skill when users ask about starting an AI company, evaluating AI startup ideas, hiring for early-stage AI startups, building products with reasoning models, finding defensibility in AI, or navigating the current AI landscape. Triggers include questions like "Should I start an AI company?", "How do I hire for my AI startup?", "Is my AI startup idea good?", "How do I compete with OpenAI/big tech?", "What should I build with AI?", or "How do I find product-market fit in AI?"

Strategic questioning framework for AI startup founders navigating AGI uncertainty. Use when founders or entrepreneurs need to evaluate AI startup ideas, assess defensibility in a rapidly changing landscape, plan strategy assuming 2-3 year AGI timelines, determine what problems remain hard when AI capabilities expand, or think through hiring/product/go-to-market decisions in the AI era. Triggers include questions like "Should I start this AI company?", "How do I plan for AGI?", "What's defensible in AI?", "How should AI change my startup strategy?", or "What questions should I ask before building an AI product?"

Strategic guidance for design-focused founders and product leaders based on Dylan Field's experience scaling Figma from a WebGL experiment to an 8-product company with 1700 employees. Use when seeking advice on founding design-tool companies, evaluating product-market fit signals, making early startup decisions (launching, pricing, pivoting), understanding how AI changes design's value proposition, integrating designers into AI product development, or applying mental models for startup leadership and product strategy. Triggered by questions about design entrepreneurship, startup scaling, product-market pull vs fit, cold outreach strategies, roadmap planning, or the future of design in AI era.

Strategic AI thinking frameworks and mental models from Satya Nadella's perspective on platform shifts, AI deployment, and building successful AI products. Use when evaluating AI strategy decisions, assessing platform opportunities, thinking through AI product positioning, considering enterprise AI deployment challenges, evaluating talent and team capabilities, or needing frameworks for justifying AI investments in terms of economic surplus. Triggers on questions about AI platform strategy, change management for AI adoption, building AI scaffolding layers, evaluating AI opportunities, or thinking through AI's societal implications.

Strategic thinking frameworks and mental models from Elon Musk for evaluating ambitious projects, applying first principles reasoning, and navigating transformative technology decisions. Use when someone asks about evaluating startup ideas, tackling seemingly impossible problems, applying first principles thinking, making career decisions about transformative technology, understanding AI timeline predictions, assessing risk/reward for ambitious ventures, managing ego and feedback loops, or decomposing complex problems into solvable components.

Reference guide for Physical Intelligence's approach to building general-purpose foundation models for robotics. Use when discussing Physical Intelligence (Pi) research methodology, explaining foundation model approaches to robotics (pre-training + post-training paradigm), comparing robotics data sources, understanding why scale alone is insufficient for robot learning, discussing pi-zero model architecture, explaining robot generalization to unseen environments, or answering questions about Chelsea Finn's work on general-purpose robotics.

Provides strategic insights on AI-driven software democratization and agent-based development trends from Replit's perspective. Use when discussing the future of software engineering, AI agent infrastructure requirements, democratization of coding, or when analyzing how AI will transform software creation from expert-only to universal access. Triggers include questions about software engineering automation trends, agent sandbox environments, SWE-bench benchmarks, or strategic implications of AI coding assistants for startups and enterprises.

Knowledge base on spatial intelligence as the next frontier in AI, based on Fei-Fei Li's insights from her Y Combinator AI Startup School talk. Use this skill when users ask about spatial intelligence concepts, 3D world modeling, the evolution of computer vision from ImageNet to World Labs, AI research strategy and problem selection, or when seeking advice on AI entrepreneurship and founding AI companies. Also trigger when discussing the relationship between vision/spatial understanding and AGI, differentiating generative vs discriminative models in 3D contexts, or exploring the data-algorithm-compute trinity for AI breakthroughs.