Does this skill support streaming responses?

Yes, it includes documentation and examples for implementing both streaming and non-streaming responses for text generation and chat.

Can I manage local models using this library?

Absolutely. The skill covers all model management functions including listing available models, pulling new ones, and deleting unused ones.

Can I use this for RAG applications?

Yes, it includes specific guidance on using the ollama.embed function to generate high-dimensional embeddings for retrieval-augmented generation.

What is the Ollama Python skill?

It is a specialized capability for Claude Code that provides implementation patterns, best practices, and code snippets for using the official Ollama Python library.

How does it handle conversation history?

The skill provides patterns for managing multi-turn conversations using a standardized message structure compatible with modern LLM interfaces.

Ollama Python Library

Name: Ollama Python Library
Author: atrawog

byatrawog

Ciencia de Datos y ML

Provides a clean, Pythonic interface for interacting with Ollama to handle text generation, chat completions, and model management.

Acerca de

This skill integrates the official Ollama Python library into Claude's workflow, enabling developers to build, manage, and interact with local large language models programmatically. It simplifies complex tasks like multi-turn conversation handling, streaming responses, and embedding generation while providing built-in utilities for model lifecycle management and performance monitoring. By using this skill, developers can leverage Claude to generate production-ready Python code that communicates seamlessly with locally hosted AI models.

Características Principales

Comprehensive local model management (list, pull, copy, delete)
Simplified multi-turn conversation state management
Seamless text generation and chat completion with streaming support
Real-time performance metrics and response evaluation
High-performance embedding generation for RAG applications
0 GitHub stars

Casos de Uso

Building Python-based AI agents or chatbots powered by local LLMs
Automating model deployment and local inference pipelines
Generating vector embeddings for local search and knowledge bases

Acerca de

Características Principales

Comprehensive local model management (list, pull, copy, delete)
Simplified multi-turn conversation state management
Seamless text generation and chat completion with streaming support
Real-time performance metrics and response evaluation
High-performance embedding generation for RAG applications
0 GitHub stars

Casos de Uso

Building Python-based AI agents or chatbots powered by local LLMs
Automating model deployment and local inference pipelines
Generating vector embeddings for local search and knowledge bases