AI Skills for Ollama

Extracts structured domain knowledge from AI models, creating a searchable reference dataset without needing an API key.

Specializes in building production-ready LLM applications and intelligent agents, optimizing AI architectures and retrieval systems.

Fetches and classifies current AI model names from various providers, ensuring up-to-date model IDs for API calls.

Facilitates the creation of production-ready AI workflows using Firebase Genkit, supporting various programming languages and AI plugins.

Specializes in building production-ready LLM applications and advanced RAG systems, integrating intelligent agents and vector search capabilities.

Transcribes audio and video from URLs using WhisperKit and analyzes transcripts with AI upon request.

Facilitates AI-powered research and document analysis by organizing materials, generating notes, and enabling context-aware interactions.

Efficiently translates SRT subtitle files using LLM APIs, supporting batch processing and cost estimation for movies and series.

Provides a comprehensive reference for AI tools in DBX Studio, aiding in tool management and integration.

Facilitates building type-safe, composable LLM applications in Ruby, optimizing AI features and workflows.

Provides expert guidance for implementing AI agents and workflows using Microsoft Agent Framework, emphasizing up-to-date documentation and integration.

Provides expert guidance for implementing AI agents and workflows using Microsoft Agent Framework, emphasizing integration and automation.

Specializes in building production-ready LLM applications and advanced RAG systems, integrating intelligent agents and vector search.

Imports GGUF models from HuggingFace into Ollama, enabling easy access and inference with various quantized models.

Enables developers to build type-safe, composable LLM applications using DSPy.rb, optimizing AI features and workflows.

Configures and optimizes the Auto-Claude Graphiti memory system for persistent context retention and semantic search across sessions.

Facilitates deployment of LLM and ML models for production inference, optimizing performance and integration with applications.

Enables the creation of offline AI systems with local models and knowledge bases, ideal for low-connectivity environments.

Open Notebook is a self-hosted tool for organizing research, generating AI insights, and maintaining data privacy.

Enables building LLM-powered React applications with generative UI, client-side tool calling, and real-time streaming capabilities.