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Found 1,211 Skills
Vision-language pre-training framework bridging frozen image encoders and LLMs. Use when you need image captioning, visual question answering, image-text retrieval, or multimodal chat with state-of-the-art zero-shot performance.
Test PydanticAI agents using TestModel, FunctionModel, VCR cassettes, and inline snapshots. Use when writing unit tests, mocking LLM responses, or recording API interactions.
Retrieval-Augmented Generation (RAG) system design patterns, chunking strategies, embedding models, retrieval techniques, and context assembly. Use when designing RAG pipelines, improving retrieval quality, or building knowledge-grounded LLM applications.
Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO) strategies for AI-powered search visibility in ChatGPT, Perplexity, Google AI Overviews, and other AI search platforms. Use when working with aeo, geo, ai search, chatgpt search, perplexity, ai overviews, generative search, llm visibility.
Expert in designing, optimizing, and evaluating prompts for Large Language Models. Specializes in Chain-of-Thought, ReAct, few-shot learning, and production prompt management. Use when crafting prompts, optimizing LLM outputs, or building prompt systems. Triggers include "prompt engineering", "prompt optimization", "chain of thought", "few-shot", "prompt template", "LLM prompting".
The essential mental models for building onchain — focused on what LLMs get wrong and what humans need explained. "Nothing is automatic" and "incentives are everything" are the core messages. Use when your human is new to onchain development, when they're designing a system, or when they ask "how does this actually work?" Also use when YOU are designing a system — the state machine + incentive framework catches design mistakes before they become dead code.
Smart contract testing with Foundry — unit tests, fuzz testing, fork testing, invariant testing. What to test, what not to test, and what LLMs get wrong.
Guidelines for deep learning development with PyTorch, Transformers, Diffusers, and Gradio for LLM and diffusion model work.
Run a free 35B AI coding agent on Apple Silicon Macs using local LLMs via llama.cpp or MLX with web search, shell, and file tools.
Implements and debugs browser Summarizer, Writer, and Rewriter integrations in JavaScript or TypeScript web apps. Use when adding availability checks, model download UX, session creation, summarize or write or rewrite flows, streaming output, abort handling, or permissions-policy constraints for built-in writing assistance APIs. Don't use for generic prompt engineering, server-side LLM SDKs, or cloud AI services.
Implements and debugs browser Proofreader API integrations in JavaScript or TypeScript web apps. Use when adding Proofreader availability checks, monitored model downloads, proofread flows, correction metadata handling, or permissions-policy checks for built-in proofreading. Don't use for generic prompt engineering, server-side LLM SDKs, or cloud AI services.
Corrective cleanup of AI-generated code — removes LLM-specific patterns while preserving behavior. Use when the user says "clean up", "deslop", "slop", "clean AI code", or when you spot LLM-generated code smells after any generation session.