context_management

Name: context_management
Availability: InStock
Author: alangeb

Monitor AI agent context windows and orchestrate subagent, fork, or background tasks.

Updated Jun 22, 2026

Search Tags:#context #optimization #delegation #memory #subagent #token

Ready to run

npx skills add https://github.com/alangeb/tau --skill context-management-alangeb

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: context_management
Source: https://github.com/alangeb/tau/tree/main/src/skills/context_management
Command: npx skills add https://github.com/alangeb/tau --skill context-management-alangeb

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill solves the issue of AI agents hitting token limits or losing performance due to bloated conversation history, ensuring efficient long-term task execution.

Core Features & Use Cases

Delegation Strategy: Provides a decision matrix to choose between subagent, fork, or background execution based on context needs.
Context Monitoring: Offers tools to track token usage, estimate capacity, and identify when to trigger compression.
Use Case: When working on a large codebase, use this skill to determine whether to spawn a subagent for a specific code review or to fork the current session to maintain complex state.

Quick Start

Use the context_management skill to analyze your current token usage and recommend the best delegation strategy for your next task.

Frequently Asked Questions about context_management

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

FAQPage Schema

How do I prevent AI agent context overflow during long tasks?▼

You prevent AI agent context overflow by monitoring token usage and triggering compression or task delegation. This skill tracks context windows and uses subagents or forks to maintain high-quality reasoning within constrained memory limits.

When should I use a subagent versus a fork for task delegation?▼

You should choose between a subagent and a fork based on a delegation decision matrix evaluating your context needs. Use a subagent for isolated code reviews, and fork the current session when you need to maintain complex state across parallel tasks.

What causes performance degradation in AI agents with large conversation histories?▼

Performance degradation in AI agents is caused by bloated conversation histories exhausting token capacity. As context windows fill, reasoning quality drops, making token usage analysis and output size control necessary to maintain efficient execution.

How do I monitor token usage to estimate remaining context capacity?▼

You monitor token usage by analyzing current context windows to estimate available capacity and identify when to trigger compression. This allows you to track consumption and proactively manage memory constraints before overflow occurs.

Can I maintain complex state in a large codebase without hitting token limits?▼

Yes, you can maintain complex state in a large codebase without hitting token limits by forking the current session or spawning background processes. This orchestration isolates tasks and prevents bloated histories from degrading agent performance.