Skill UI

This skill automates the setup and configuration of Ollama, allowing users to deploy Large Language Models (LLMs) locally. It performs system assessment (OS, GPU, RAM), selects optimal models based on hardware availability, and handles installation across macOS, Linux, and Docker. Use this when you require free, self-hosted AI alternatives, need offline inference capabilities, or wish to eliminate reliance on costly hosted API services.

An expert guide for deploying and optimizing Large Language Models (LLMs) entirely on local hardware. Mastering the entire local AI ecosystem, including inference engines like Ollama, vLLM, and llama.cpp. Provides deep knowledge on quantization formats (GGUF, EXL2, AWQ), VRAM calculation, and ensuring privacy-first, offline deployment for state-of-the-art models.

bdistill transforms AI interactions into a structured, queryable knowledge base. It extracts, structures, and quality-scores domain knowledge from both closed-source AI models (in-session) and local open-source models (via Ollama). Users can build comprehensive reference datasets, Q&A pairs, or generate structured training data for traditional machine learning, ensuring validated and exportable domain expertise.

Format chat messages into local model templates (llama3, mistral, chatml, phi, gemma, or a model id) and route queries through a small WASM HNSW pattern index. Useful for preparing prompts for llama.cpp, Ollama, and other local runtimes.

Extract structured domain knowledge from AI models in-session or via Ollama, with quality scoring, search, export, and local model support. No API key needed for closed models.

An expert guide for deploying and optimizing Large Language Models (LLMs) entirely on local hardware. Mastering the entire local AI ecosystem, including inference engines like Ollama, vLLM, and llama.cpp. Provides deep knowledge on quantization formats (GGUF, EXL2, AWQ), VRAM calculation, and ensuring privacy-first, offline deployment for state-of-the-art models.

Deploy a local LLM stack with Ollama, Open WebUI, and LiteLLM for offline, privacy-first AI workloads. Covers installation, model selection, Docker Compose setup, API usage, and custom Modelfiles for development environments.

Calculate the cost per million tokens for running AI models locally on your own hardware like Ollama or llama.cpp. It factors in electricity prices, hardware costs, power consumption, and generation speed to determine the break-even point against hosted API models. Useful for deciding whether to deploy models locally or use cloud services based on actual usage and financial constraints.