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harshaldhaduk
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V3 Swarm Coordination

Coordinates 15 specialized AI agents across phases for GitHub-tracked v3 implementations.

1|1|Updated Apr 11, 2026
Search Tags:#dependency management#agent orchestration#parallel development#performance monitoring#swarm coordination#github integration#v3 implementation
One-click install
npx skills add https://github.com/harshaldhaduk/Lattice --skill v3-swarm-coordination-harshaldhaduk
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: V3 Swarm Coordination
Source: https://github.com/harshaldhaduk/Lattice/tree/main/.claude/skills/v3-swarm-coordination
Command: npx skills add https://github.com/harshaldhaduk/Lattice --skill v3-swarm-coordination-harshaldhaduk

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Coordinating large parallel AI agent swarms for complex v3 implementation projects often leads to deadlocks, duplicated work, timeline delays, and unmanageable coordination overhead, especially when overseeing 15 specialized agents across multiple domains.

Core Features & Use Cases

  • Hierarchical Mesh Topology: Queen coordinator-led 15-agent structure organized into security, core, integration, quality, performance and deployment domains for clear ownership.
  • Phase-Based Parallel Execution: Structured 4-phase workflow (foundation, core systems, integration, release) with automated dependency resolution to eliminate blocking and deadlocks.
  • Use Case: For a 14-week claude-flow v3 implementation with 10 ADRs, this skill coordinates all 15 specialized agents to work in parallel without conflicts, delivering all requirements on schedule.

Quick Start

Use the v3-swarm-coordination skill to initialize and execute the full 15-agent hierarchical mesh for your v3 implementation project.

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 parallel AI agents to prevent deadlocks in software implementation?▼

Parallel AI agent coordination eliminates deadlocks by applying a hierarchical mesh topology with a queen coordinator and phase-based execution to automate dependency resolution across specialized domains. This structured approach prevents blocking and duplicated work.

What is the best way to manage dependency resolution for large parallel AI swarms?▼

Dependency resolution for large parallel AI swarms is managed through a structured 4-phase workflow covering foundation, core systems, integration, and release. This automated phase-based execution tracks dependencies to eliminate blocking during complex software implementation.

Can I use GitHub milestone tracking to monitor parallel efficiency in AI agent swarms?▼

GitHub milestone tracking integrates with AI agent swarms to monitor parallel efficiency and ensure utilization remains above 85%. This integration tracks progress across 15 specialized agents working concurrently across security, core, integration, quality, performance and deployment domains.

Does parallel agent orchestration work for complex multi-week development workflows?▼

Parallel agent orchestration supports complex 14-week v3 development workflows by organizing 15 specialized agents into a hierarchical mesh. It coordinates phase-based execution and architectural decision records to deliver all project requirements on schedule without conflicts.

How do I structure a 15-agent AI swarm for software development without timeline delays?▼

A 15-agent AI swarm is structured using a hierarchical mesh topology led by a queen coordinator, organizing agents into security, core, integration, quality, performance, and deployment domains. This structure ensures clear ownership and eliminates timeline delays.

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