Why should I avoid using TaskOutput?

TaskOutput returns the entire transcript of an agent's work, which can often exceed 70,000 tokens. Using file-based checks or running tests is a much more token-efficient way to verify work.

Why is agent context isolation important?

It prevents large sub-agent outputs from consuming your main conversation's token budget, which prevents Claude from 'forgetting' earlier instructions or project context due to session compaction.

How can I tell if a background agent is finished?

You can monitor system progress reminders, check the growth of the task output file in /tmp/, or poll the cache directory for the expected output file.

How do agents communicate using this skill?

Instead of returning text to the main chat, agents write their results to specific files in the .maestro/cache/agents/ directory, which subsequent agents can then read as needed.

Maestro Agent Context Isolation

Name: Maestro Agent Context Isolation
Author: scooter-lacroix

byscooter-lacroix

Productivity & Workflow

Optimizes Claude's context window by isolating sub-agent outputs through file-based coordination and background execution.

About

Maestro Agent Context Isolation provides a specialized framework for managing complex multi-agent workflows without exhausting Claude's context window. By enforcing background execution and file-based communication, it prevents massive sub-agent transcripts from flooding the primary conversation. This approach ensures that critical project history and user instructions remain intact, avoiding the common pitfalls of mid-session context compaction and lost tokens during intensive engineering tasks.

Key Features

Context-budget preservation for long-running AI sessions
Verification-first workflow using direct test suite execution
Background agent execution for strict context isolation
File-based coordination via structured caching directories
Automated task progress monitoring through system reminders
0 GitHub stars

Use Cases

Executing multi-stage research and planning without losing session history
Managing large-scale code refactors involving multiple specialized sub-agents
Preventing context flooding when processing tasks with high token outputs

About

Key Features

Context-budget preservation for long-running AI sessions
Verification-first workflow using direct test suite execution
Background agent execution for strict context isolation
File-based coordination via structured caching directories
Automated task progress monitoring through system reminders
0 GitHub stars

Use Cases

Executing multi-stage research and planning without losing session history
Managing large-scale code refactors involving multiple specialized sub-agents
Preventing context flooding when processing tasks with high token outputs