V3 Swarm Coordination

Orchestrates a 15-agent hierarchical mesh swarm for parallel v3 implementation across security, core, and integration domains.

Updated Aug 18, 2026
One-click install
npx skills add https://github.com/wandreandrade2018-maker/claude-config --skill v3-swarm-coordination-wandreandrade2018-maker
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: V3 Swarm Coordination
Source: https://github.com/wandreandrade2018-maker/claude-config/tree/main/.claude/skills/v3-swarm-coordination
Command: npx skills add https://github.com/wandreandrade2018-maker/claude-config --skill v3-swarm-coordination-wandreandrade2018-maker

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Coordinating a large multi-agent implementation effort across security, core systems, integration, quality, performance, and release domains is error-prone: dependencies deadlock, agents sit idle, and timelines slip. This Skill provides a structured 15-agent hierarchical mesh coordination plan with explicit phases, dependency graphs, and communication patterns to keep parallel execution on track. ## Core Features & Use Cases - 15-Agent Swarm Architecture: Defines a queen coordinator plus security, core, integration, quality, performance, and deployment agents with a clear roster and phase assignments. - Phase-Based Execution: Four phases (Foundation, Core Systems, Integration, Release) mapped to a 14-week timeline with parallel Task() dispatch examples. - Dependency & Communication Patterns: TypeScript reference implementations for dependency resolution with deadlock detection, GitHub milestone/issue tracking, QUIC message bus broadcasting, load balancing, and parallel efficiency monitoring. - Use Case: A team implementing claude-flow v3 across 10 ADRs uses this Skill to spawn the full swarm, dispatch CVE remediation and AgentDB memory unification in parallel, and track progress via automated GitHub issues until the v3.0.0 release. ## Quick Start Initialize the 15-agent v3 swarm by dispatching a queen coordinator task, then launch the Phase 1 security and architecture tasks in parallel.

Frequently Asked Questions about V3 Swarm Coordination

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

FAQPage Schema
How do I coordinate multiple AI agents in parallel for a large implementation?

Use a hierarchical mesh topology with a queen coordinator dispatching domain-specific agents via parallel Task() calls. Group agents by domain (security, core, integration) and execute them in phases so independent work runs concurrently while dependencies are respected.

How do I prevent deadlocks in multi-agent dependency chains?

Model agent dependencies as a directed graph and only execute agents whose dependencies have completed. If no agent is ready while work remains, the coordination loop detects the deadlock and throws an error instead of blocking indefinitely.

What phases should a multi-agent software release follow?

This plan uses four phases: Foundation (security and architecture), Core Systems (memory, swarm, MCP), Integration (CLI, neural learning, benchmarking), and Release (optimization, CI/CD, final testing). Each phase activates a defined subset of the 15 agents.

How do I track progress of a multi-agent swarm in GitHub?

Create a milestone for the release, open epic issues labeled per agent, and assign them to the responsible agents. Automated hourly progress posts keep issue status current without manual updates.

What are the limitations of a 15-agent swarm approach?

Coordination overhead grows with agent count, so parallel efficiency must stay above 85 percent utilization to be worthwhile. Highly sequential work or small codebases gain little from swarm decomposition and may be faster with fewer agents.