moai-foundation-context

Manage token budgets and persist session state across multi-agent workflows.

Updated Feb 22, 2026
One-click install
npx skills add https://github.com/SayBGM/TubeExtract --skill moai-foundation-context-saybgm
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: moai-foundation-context
Source: https://github.com/SayBGM/TubeExtract/tree/main/.claude/skills/moai-foundation-context
Command: npx skills add https://github.com/SayBGM/TubeExtract --skill moai-foundation-context-saybgm

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill tackles the challenge of managing limited token budgets and maintaining session continuity in complex, multi-agent AI workflows, preventing context overflow and ensuring smooth state transitions.

Core Features & Use Cases

  • Token Budget Management: Intelligently allocates and monitors a 200K token budget, optimizing usage and preventing overflow.
  • Session State Persistence: Saves and restores session state across interruptions or /clear commands, allowing for seamless resumption of long-running tasks.
  • Multi-Agent Handoffs: Facilitates efficient context transfer between different AI agents, minimizing token overhead and ensuring compatibility.
  • Progressive Disclosure: Loads context incrementally based on relevance, further optimizing token usage.
  • Use Case: When running a long-running SPEC-First workflow that spans multiple agents and requires context to be preserved across several /clear commands, this Skill ensures that critical information is retained and token usage remains within limits.

Quick Start

Use the moai-foundation-context skill to manage token budget and session state for long-running workflows.

Frequently Asked Questions about moai-foundation-context

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

FAQPage Schema
How do I manage token budget limits in multi-agent AI workflows?

Token budget management in multi-agent workflows intelligently allocates and monitors a 200K token limit, optimizing usage and preventing context overflow. This ensures long-running tasks stay within token constraints while retaining critical information.

What is session state persistence and when do I need it for AI agents?

Session state persistence saves and restores context across interruptions or clearing commands. You need it when running long-running workflows that span multiple agents and require critical information to be retained without context loss.

How do I preserve context across multiple clear commands in a long-running workflow?

To preserve context across clear commands, use session state persistence to save and restore session state. This allows seamless resumption of long-running tasks while maintaining token budget allocation and preventing context overflow.

Does progressive disclosure help optimize token usage in enterprise-grade AI systems?

Progressive disclosure optimizes token usage by loading context incrementally based on relevance. This approach minimizes token overhead during multi-agent handoffs and keeps complex enterprise-grade workflows within the 200K token limit.

What is the best way to handle context transfer between different AI agents?

The best way to handle multi-agent context transfer is using efficient handoff mechanisms that minimize token overhead and ensure compatibility. This prevents context overflow and maintains smooth state transitions between agents.

Why does my long-running AI workflow lose state after context window clearing?

Long-running workflows lose state after clearing because session context is not automatically persisted. Implementing session state persistence saves and restores critical information, allowing seamless workflow resumption across clearing commands.