planning_agent

Decompose complex user instructions into a structured multi-agent execution graph.

4|Updated Feb 25, 2026
One-click install
npx skills add https://github.com/dewitt/swarm --skill planning-agent-dewitt
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: planning_agent
Source: https://github.com/dewitt/swarm/tree/main/skills/planning-agent
Command: npx skills add https://github.com/dewitt/swarm --skill planning-agent-dewitt

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Planning complex user requests manually is error-prone and time-consuming; this Skill provides a principled way to decompose tasks into an executable multi-agent plan with persistent state coordination.

Core Features & Use Cases

  • Decomposes complex goals into sequential and parallel agent spans across specialized roles.
  • Maintains a persistent Session State to preserve context across turns.
  • Produces a final synthesis node to unify results and deliver a cohesive plan.

Quick Start

Provide a complex user task and planning_agent will generate a multi-agent execution graph to accomplish it.

Frequently Asked Questions about planning_agent

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

FAQPage Schema
What is multi-agent planning and when do I need it for workflow orchestration?▼

Multi-agent planning decomposes complex instructions into a structured execution graph across specialized agents. You need it when tasks require cross-agent collaboration, persistent session state, and deterministic planning with explicit dependencies to ensure accurate execution.

How do I decompose complex requests into a multi-agent execution graph?▼

You decompose requests by providing a complex task to a planning agent, which generates a strict DAG with named spans and a terminal synthesis node. It outputs a defined JSON schema coordinating sequential and parallel agent spans across specialized roles.

Can I maintain persistent session state across multiple agent turns during task decomposition?▼

Yes, you can maintain persistent session state to preserve context across turns. The decomposition process coordinates specialized Swarm agents by enforcing a strict DAG with named spans, ensuring context is retained throughout the execution graph.

Does multi-agent planning work with parallel and sequential execution spans?▼

Yes, multi-agent planning works with both parallel and sequential execution spans. It decomposes complex goals into specialized agent roles within a strict DAG, coordinating cross-agent collaboration while maintaining persistent session state throughout the workflow.

What are the limitations of using a strict DAG for multi-agent workflow orchestration?▼

A strict DAG imposes deterministic planning with explicit dependencies and a terminal synthesis node, meaning it does not support dynamic cyclic routing or runtime loop modifications. Complex goals must fit within defined safety constraints and a static execution graph structure.