What is a fallback chain in LLM management?

A fallback chain is a reliability pattern where the system attempts a request with a primary model and automatically switches to pre-defined alternatives if the primary provider experiences an error or timeout.

Which LLM providers are supported?

The skill currently includes configurations for major providers including Anthropic, OpenAI, and Google, with the ability to register custom or local models.

How does this skill help reduce AI costs?

It estimates costs per million tokens and routes simple tasks to cheaper 'fast' tier models while using flagship models only when necessary for complex reasoning.

Can I set custom routing rules?

Yes, you can define logic-based routing rules that check for prompt length, specific keywords, or required capabilities like vision or function calling to select the optimal model.

LLM Model Selection & Provider Management

Name: LLM Model Selection & Provider Management
Author: ricardoroche

byricardoroche

Data Science & ML

Optimizes LLM performance and reliability through automated model selection, cost estimation, and intelligent fallback strategies.

About

This skill provides a robust framework for managing multiple Large Language Model (LLM) providers like Anthropic, OpenAI, and Google. It streamlines the selection process by comparing model capabilities, context windows, and pricing tiers in real-time. By implementing intelligent routing rules and reliable fallback chains, it ensures that your AI applications remain cost-effective and resilient against provider downtime while always choosing the best-suited model for any given task or budget requirement.

Key Features

Automated Fallback Chains to ensure high availability and graceful recovery from API errors
0 GitHub stars
Unified Model Registry for tracking capabilities, pricing, and context windows across providers
Intelligent Model Routing based on task complexity, prompt length, and specific requirements
Real-time Cost Optimization and estimation for input/output token usage
Support for multi-provider strategies including Anthropic, OpenAI, and Google

Use Cases

Budget-conscious development through predictive cost estimation before executing large-scale LLM workflows
Routing simple queries to cost-effective models like Claude Haiku while reserving flagship models for complex reasoning
Implementing a high-reliability system that automatically switches to a secondary provider if the primary API fails

About

Key Features

Automated Fallback Chains to ensure high availability and graceful recovery from API errors
0 GitHub stars
Unified Model Registry for tracking capabilities, pricing, and context windows across providers
Intelligent Model Routing based on task complexity, prompt length, and specific requirements
Real-time Cost Optimization and estimation for input/output token usage
Support for multi-provider strategies including Anthropic, OpenAI, and Google

Use Cases

Budget-conscious development through predictive cost estimation before executing large-scale LLM workflows
Routing simple queries to cost-effective models like Claude Haiku while reserving flagship models for complex reasoning
Implementing a high-reliability system that automatically switches to a secondary provider if the primary API fails