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Session Context

Manage LLM provider lifecycle and connectivity within MCP-compliant architectures.

2|Updated Sep 2, 2025
Search Tags:#automation#llm#mcp#orchestration#inference#gateway
One-click install
npx skills add https://github.com/sandraschi/local-llm-mcp --skill session-context-sandraschi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Session Context
Source: https://github.com/sandraschi/local-llm-mcp/tree/main/.opencode/skills/session-context
Command: npx skills add https://github.com/sandraschi/local-llm-mcp --skill session-context-sandraschi

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill solves the complexity of managing multiple local and cloud-based LLM providers, ensuring that models are healthy, reachable, and properly configured for agentic tasks.

Core Features & Use Cases

  • Provider Health Monitoring: Automatically verifies connectivity to 28+ AI gateways and services.
  • Model Lifecycle Management: Simplifies the discovery, loading, and unloading of models across local and remote peers.
  • Use Case: Before starting a coding session, use this skill to verify that your local LM Studio instance is linked and that your preferred cloud provider is responsive, preventing runtime errors during complex reasoning tasks.

Quick Start

Use the session context skill to check the health of all configured providers and list the currently available models.

Frequently Asked Questions about Session Context

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

FAQPage Schema
How do I check LLM provider health before starting an agentic coding session?▼

To check LLM provider health, this skill verifies connectivity to configured gateways and services, ensuring local and cloud models are reachable before you start agentic coding tasks to prevent runtime errors.

What is the best way to manage local and cloud LLM workflows in an MCP architecture?▼

Managing local and cloud LLM workflows in an MCP architecture involves coordinating model discovery, health verification, and resource cleanup across distributed AI services to maintain operational stability.

Does this LLM orchestration skill work with FastMCP and active gateway configurations?▼

Yes, this LLM orchestration skill requires integration with FastMCP and active gateway configurations to manage the lifecycle and connectivity of local and cloud-based providers properly.

Can I discover and unload models across remote peers during an inference task?▼

Yes, you can discover and unload models across remote peers during inference tasks, as the skill simplifies model lifecycle management and resource cleanup across distributed AI services.

Why do I need to verify connectivity to AI gateways before running complex reasoning tasks?▼

Verifying connectivity to AI gateways before complex reasoning tasks prevents runtime errors by automatically confirming that your preferred cloud provider and local instances are responsive and properly configured.

What are the limitations of orchestrating LLM workflows without active gateway configurations?▼

Orchestrating LLM workflows without active gateway configurations limits operational stability, preventing the skill from maintaining connectivity and managing model lifecycles across distributed AI services.

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