subagent_manager

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

3|Updated Feb 4, 2026
One-click install
npx skills add https://github.com/oneles/openclaw-skill-subagent-manager --skill subagent-manager
Or copy as Structured Prompt for Agent
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.