Does this skill work for sequential tasks?

Yes, it uses a workflow called 'Sequential Gates' to ensure that if one task depends on another, the subagents are launched in the correct order while still maintaining context isolation.

What is the primary benefit of using Subagent Teams?

The primary benefit is context preservation. By delegating heavy tasks to subagents, you prevent the main context window from filling up, which stops Claude from auto-compacting and losing the 'memory' of your high-level project goals.

How does the skill decide which AI model to use?

It follows a cost-performance strategy: Haiku is used for simple searches, Sonnet for architecture and analysis, and Opus for complex reasoning and implementation.

When should I avoid using Subagent Teams?

Avoid using this skill for trivial, single-step tasks where the overhead of delegating to a subagent would take longer than simply performing the task in the main window.

Subagent Teams Orchestrator

Name: Subagent Teams Orchestrator
Author: alijilani-dev

byalijilani-dev

生产力与工作流

Orchestrates complex development tasks by delegating work to specialized parallel subagents to preserve context and maximize reasoning quality.

关于

The Subagent Teams skill is designed to maintain peak Claude performance during large-scale coding projects by offloading heavy lifting to independent sub-sessions. By decomposing intricate requests into specialized tasks—such as codebase exploration, testing, and implementation—this skill prevents the main context window from becoming bloated and triggering quality-degrading auto-compacting. It allows for parallel execution of subtasks using optimized model selection (Haiku, Sonnet, or Opus), ensuring that the main orchestrator remains focused on high-level decision-making and synthesis.

主要功能

Dynamic model selection based on task complexity (Opus, Sonnet, Haiku)
Automated synthesis of results from multiple background agents
Strategic sequential gating for tasks with strict dependencies
Parallel task decomposition and execution via specialized subagents
Context isolation to prevent main window auto-compact and quality loss
0 GitHub stars

使用场景

Performing deep codebase exploration and pattern research without losing main context
Executing multi-file refactors where testing and implementation can run in parallel
Managing complex feature requests that require simultaneous research, planning, and execution

关于

主要功能

Dynamic model selection based on task complexity (Opus, Sonnet, Haiku)
Automated synthesis of results from multiple background agents
Strategic sequential gating for tasks with strict dependencies
Parallel task decomposition and execution via specialized subagents
Context isolation to prevent main window auto-compact and quality loss
0 GitHub stars

使用场景

Performing deep codebase exploration and pattern research without losing main context
Executing multi-file refactors where testing and implementation can run in parallel
Managing complex feature requests that require simultaneous research, planning, and execution