Can I resume work after a system interruption?

Yes, the skill utilizes session state layers and snapshots to allow seamless recovery of your current implementation phase and project context.

How does this skill prevent performance degradation?

It monitors token usage in real-time and triggers aggressive context clearing or compression before usage reaches the 150K-180K threshold, ensuring the model remains responsive and accurate.

Why is progressive disclosure important?

By loading files based on priority tiers rather than all at once, the skill preserves your token budget for actual coding rather than wasting it on irrelevant background files.

How does multi-agent handoff work?

It packages critical session information, task context, and recovery tokens into a standardized JSON format that allows one agent to pick up exactly where another left off with minimal overhead.

Enterprise Context & Session Manager

Name: Enterprise Context & Session Manager
Author: modu-ai

bymodu-ai

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Productivity & Workflow

Optimizes Claude Code's 200K token context window through intelligent session persistence, budget monitoring, and multi-agent handoff protocols.

About

The moai-foundation-context skill provides a professional-grade framework for managing long-running Claude Code sessions, ensuring peak model performance by preventing context overflow and token waste. It implements sophisticated token budget allocation, state persistence for session recovery, and progressive disclosure patterns that keep the workspace lean. Ideal for enterprise-scale development, it automates strategic context clearing and facilitates seamless handoffs between specialized AI agents while maintaining critical project milestones and architectural state.

Key Features

Multi-agent handoff protocols for seamless context transfer
Session state persistence and recovery for interrupted workflows
Real-time 200K token budget monitoring with automated usage alerts
16 GitHub stars
Automated context clearing and compression at strategic checkpoints
Progressive file loading and priority-based caching strategies

Use Cases

Coordinating complex multi-step workflows between different AI agent roles
Maintaining session continuity during model switches or environment resets
Managing large-scale enterprise codebases that exceed standard context limits

About

Key Features

Multi-agent handoff protocols for seamless context transfer
Session state persistence and recovery for interrupted workflows
Real-time 200K token budget monitoring with automated usage alerts
16 GitHub stars
Automated context clearing and compression at strategic checkpoints
Progressive file loading and priority-based caching strategies

Use Cases

Coordinating complex multi-step workflows between different AI agent roles
Maintaining session continuity during model switches or environment resets
Managing large-scale enterprise codebases that exceed standard context limits