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Found 1,644 Skills
Running and fine-tuning LLMs on Apple Silicon with MLX. Use when working with models locally on Mac, converting Hugging Face models to MLX format, fine-tuning with LoRA/QLoRA on Apple Silicon, or serving models via HTTP API.
Access and interact with Large Language Models from the command line using Simon Willison's llm CLI tool. Supports OpenAI, Anthropic, Gemini, Llama, and dozens of other models via plugins. Features include chat sessions, embeddings, structured data extraction with schemas, prompt templates, conversation logging, and tool use. This skill is triggered when the user says things like "run a prompt with llm", "use the llm command", "call an LLM from the command line", "set up llm API keys", "install llm plugins", "create embeddings", or "extract structured data from text".
Router skill for LLMQuant market-intelligence workflows. Use when the user needs macro views, market sentiment dashboards, or event probability signals.
Best practices for LLM-assisted coding. Declarative workflows, simplicity, tenacity.
Large Language Model development, training, fine-tuning, and deployment best practices.
Guides LLM agents through large-scale coding tasks using a spec-driven, phase-by-phase methodology covering requirement definition, planning, algorithm design, and implementation with OOP principles and language-specific coding standards. Use when starting a new software project, implementing a complex feature, refactoring existing code, or when you need a disciplined step-by-step approach to any non-trivial coding task.
Analyzes and generates llms.txt files -- the emerging standard for helping AI systems understand website structure and content. Can validate existing llms.txt files or generate new ones from scratch by crawling the site.
Reduce LLM API and infrastructure costs through model selection, prompt caching, batching, caching, quantization, and self-hosting strategies. Track spend by team and model, set budgets, and implement cost-aware routing.
Deploys ML and LLM models on TrueFoundry with GPU inference servers (vLLM, TGI, NVIDIA NIM). Uses YAML manifests with `tfy apply`. Use when serving language models, deploying Hugging Face models, or hosting GPU-accelerated inference endpoints.
Router skill for LLMQuant equity derivatives workflows. Use when the user needs single-stock derivative, convertible, warrant, structured payoff, or hybrid security analysis.
Strategies for managing LLM context windows effectively in AI agents. Use when building agents that handle long conversations, multi-step tasks, tool orchestration, or need to maintain coherence across extended interactions.
Local LLM operations with Ollama on Apple Silicon, including setup, model pulls, chat launchers, benchmarks, and diagnostics.