coordinated-agent-teams

Design and validate multi-agent implementation plans with task dependencies and parallelism.

104|31|Updated Dec 10, 2025

Search Tags:#verification #multi-agent #software architecture #parallelism #dependency ordering

Ready to run

npx skills add https://github.com/jacob-dietle/context-os --skill coordinated-agent-teams

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: coordinated-agent-teams
Source: https://github.com/jacob-dietle/context-os/tree/main/.claude/skills/coordinated-agent-teams
Command: npx skills add https://github.com/jacob-dietle/context-os --skill coordinated-agent-teams

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This skill helps developers and architects design multi-agent implementations with parallelism, dependency ordering, and robust verification strategies, reducing integration surprises and context loss.

Core Features & Use Cases

Agent DAG Decomposition: Break down complex specifications into a directed acyclic graph (DAG) of interdependent tasks.
Dependency Ordering: Determine the order of task execution to ensure correct integration.
Parallelism: Leverage parallel execution for efficiency in scenarios with multiple agents.
Verification: Apply evidence-based strategies for contract testing and error recovery.
Use Case: For a large software project with multiple modules, use this skill to create a plan that assigns different modules to different teams or agents, ensuring a smooth handoff between them.

Quick Start

Start by defining the problem context for your multi-agent system, then run the coordinated-agent-teams skill to generate an implementation plan.

Frequently Asked Questions about coordinated-agent-teams

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

FAQPage Schema

How do I decompose complex software tasks into a multi-agent DAG for parallel execution?▼

Multi-agent DAG decomposition breaks complex specifications into a directed acyclic graph of interdependent tasks. It determines task execution order to ensure correct integration and leverages parallel execution for efficiency across multiple agents.

How do I set up dependency ordering and parallelism strategies for multi-agent software architecture?▼

Dependency ordering and parallelism strategies are established by mapping task execution sequences and assigning parallel execution paths within an implementation plan. This ensures correct module integration and smooth handoffs between assigned agents.

What verification methods should I use for multi-agent system integration and error recovery?▼

Evidence-based verification strategies like contract testing and error recovery validate multi-agent system integration. These methods prevent integration surprises and context loss by ensuring robust handoffs between parallel agents.

When do I need to design a multi-agent implementation plan with directed acyclic graph task decomposition?▼

A multi-agent implementation plan is needed for large software projects with multiple modules requiring parallel execution. It prevents integration surprises and context loss by ensuring smooth handoffs and coordinated task execution across assigned agents.

Can I use coordinated agent teams to manage context loss and integration surprises in parallel software development?▼

Yes, designing multi-agent implementations with parallelism and dependency ordering reduces integration surprises and context loss. It assigns different modules to different agents or teams, ensuring smooth handoffs and robust verification during parallel software development.