subagent_manager

Coordinates specialized AI sub-agents for parallel task execution and progress reporting.

3|Updated Feb 4, 2026

Search Tags:#task management #parallel processing #monitoring #delegation #agent #manager

Ready to run

npx skills add https://github.com/oneles/openclaw-skill-subagent-manager --skill subagent-manager

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: subagent_manager
Source: https://github.com/oneles/openclaw-skill-subagent-manager/tree/main
Command: npx skills add https://github.com/oneles/openclaw-skill-subagent-manager --skill subagent-manager

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill streamlines complex task management by delegating work to specialized sub-agents, ensuring efficient parallel execution and providing timely progress updates to the user.

Core Features & Use Cases

Parallel Task Delegation: Breaks down large tasks and assigns them to multiple sub-agents for concurrent processing.
Automated Progress Reporting: Provides regular updates on long-running tasks, keeping the user informed.
Managerial Focus: Acts as a supervisor, coordinating sub-agents rather than performing the tasks directly.
Use Case: A user requests a complex research report. The manager skill spawns sub-agents to gather data, analyze findings, and draft sections concurrently, reporting progress along the way.

Quick Start

Use the subagent_manager skill to write a 5000-word essay on artificial intelligence.

Frequently Asked Questions about subagent_manager

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

FAQPage Schema

How do I delegate tasks to multiple agents for parallel processing?▼

Parallel processing of tasks requires a supervisor-subagent architecture that decomposes large requests, assigns subtasks to specialized sub-agents concurrently, monitors execution, and synthesizes the collected results. This manager-subagent structure optimizes complex workflows for speed and depth.

What is a manager-subagent architecture for AI workflows?▼

A manager-subagent architecture is a coordination model where a central supervisor delegates subtasks to specialized sub-agents rather than executing them directly. This structure enables concurrent task processing, automated progress reporting, and result synthesis for complex workflows.

How do I monitor progress and synthesize results from concurrent sub-agents?▼

Monitoring progress and synthesizing results from concurrent sub-agents requires a managerial coordination layer that provides automated progress reporting during execution and aggregates outputs upon completion. The supervisor handles scheduling and merges results automatically.

Can I use task decomposition to break down large research reports into concurrent subtasks?▼

Task decomposition breaks down large research reports into smaller subtasks assigned to sub-agents. A manager skill spawns sub-agents to gather data, analyze findings, and draft sections concurrently, reporting progress along the way to optimize speed and depth.

Does this parallel task delegation approach work without external dependencies?▼

Parallel task delegation works without external dependencies. The supervisor-subagent architecture operates using internal scripts for task management, scheduling, and monitoring, requiring no additional external packages or frameworks to execute complex workflows.

What are the limitations of using a supervisor-subagent architecture for task management?▼

A limitation of the supervisor-subagent architecture is that the manager coordinates and monitors rather than performing tasks directly. Task execution quality depends entirely on the specialized sub-agents' capabilities, and synthesizing concurrent outputs may introduce processing overhead.