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stacks-ai

Automate AI integration for Stacks applications with multiple provider drivers.

622|17|Updated Apr 26, 2022
Search Tags:#deployment#embeddings#ai#llm#rag#stacks#drivers
One-click install
npx skills add https://github.com/stacksjs/stacks --skill stacks-ai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: stacks-ai
Source: https://github.com/stacksjs/stacks/tree/main/.claude/skills/stacks-ai
Command: npx skills add https://github.com/stacksjs/stacks --skill stacks-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Streamlines the integration of AI features into Stacks applications by providing a unified driver suite, ready-to-use AI utilities, and a scalable configuration model.

Core Features & Use Cases

  • Multiple AI drivers: Anthropic, OpenAI, Ollama, and AWS Bedrock for chat, embeddings, and completions.
  • AI tooling and capabilities: image generation, vision analysis, RAG/vector search, embeddings, MCP (Model Context Protocol) clients, personalization, sentiment analysis, and content classification.
  • Use cases: rapid prototyping of intelligent frontends, backends, and cloud workflows with consistent AI integration and deployment patterns.

Quick Start

Install and configure the AI module to enable provider drivers and local capabilities in your Stacks project.

Frequently Asked Questions about stacks-ai

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I integrate AI capabilities like LLM chat and embeddings into a Stacks application?▼

You can integrate AI capabilities into Stacks apps by configuring the core AI package via config/ai.ts. This provides unified driver suites for Anthropic, OpenAI, Ollama, and AWS Bedrock, enabling chat, embeddings, and completions.

What is the best way to run RAG and vector search within a Stacks frontend or backend workflow?▼

The best way to run RAG and vector search in Stacks is using the built-in AI tooling, which provides ready-to-use utilities for embeddings and vector retrieval across frontend, backend, and cloud workflows.

Can I use Ollama and AWS Bedrock drivers together for AI completions in the same project?▼

Yes, you can use Ollama and AWS Bedrock drivers together. The AI module supports multiple provider drivers simultaneously, allowing you to route completions, chat, and embeddings across different platforms.

Does the Stacks AI integration support image generation and vision analysis?▼

Yes, Stacks AI integration supports image generation and vision analysis. It includes ready-to-use AI utilities for these capabilities, alongside sentiment analysis, personalization, and content classification.

How do I configure an MCP client for Model Context Protocol resources in a Stacks project?▼

To configure an MCP client in Stacks, use the core AI package and define your settings in config/ai.ts. This allows you to manage MCP resources and connect external tools to your LLM workflows.

Do I need external dependencies to use sentiment analysis and personalization features in Stacks?▼

No external dependencies are required for sentiment analysis and personalization. The Stacks AI module includes these as built-in capabilities, configured directly through the core AI package without additional packages.

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