swarm-advanced

Orchestrate distributed workflows across mesh, hierarchical, star, and ring topologies.

Updated Mar 7, 2026

Search Tags:#workflow #development #research #orchestration #distributed #swarm #mult-agent

Ready to run

npx skills add https://github.com/nickm538/wifi-sensing-advanced --skill swarm-advanced-nickm538

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: swarm-advanced
Source: https://github.com/nickm538/wifi-sensing-advanced/tree/main/.claude/skills/swarm-advanced
Command: npx skills add https://github.com/nickm538/wifi-sensing-advanced --skill swarm-advanced-nickm538

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Orchestrate advanced swarm patterns for distributed research, development, and testing workflows.

Core Features & Use Cases

Advanced swarm patterns for research, development, testing, and experimentation.
Supports multiple topologies (mesh, hierarchical, star, ring) and agent roles for scalable orchestration.
Real-world use cases include coordinating distributed experiments, automated task orchestration, and reproducible workflows.

Quick Start

Install Claude Flow and load the swarm templates to begin orchestrating multi-agent experiments.

Frequently Asked Questions about swarm-advanced

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

FAQPage Schema

How do I orchestrate multi-agent workflows for distributed research and testing?▼

Multi-agent orchestration coordinates MCP tools and CLI commands across mesh, hierarchical, star, and ring topologies to enable scalable distributed experiments and reproducible outcomes. It enforces YAML frontmatter metadata and supports optional scripts, references, and assets directories for deterministic task execution.

What swarm topology patterns are available for distributed development workflows?▼

Available swarm topology patterns include mesh, hierarchical, star, and ring configurations. Each topology supports different agent roles and coordination strategies, enabling scalable orchestration for automated task distribution, distributed experiments, and reproducible development workflows across coordinated MCP tools and CLI commands.

How do I set up deterministic task execution for multi-agent experiments?▼

Deterministic task execution requires YAML frontmatter metadata with name and description fields, plus optional scripts, references, and assets directories. These structured inputs ensure reproducible outcomes across distributed swarm orchestration by providing consistent guidance for memory management, monitoring, and automation.

Can I coordinate MCP tools and CLI commands across multiple agents in a swarm?▼

Yes, swarm orchestration coordinates MCP tools and CLI-based commands across multiple agents using mesh, hierarchical, star, and ring topologies. This enables scalable experiments and reproducible outcomes for distributed research, development, and testing workflows with structured memory, monitoring, and automation guidance.

What's the best way to structure metadata for reproducible distributed workflows?▼

Reproducible distributed workflows require YAML frontmatter metadata enforcing name and description fields. Optional scripts, references, and assets directories provide additional structure for deterministic tasks, while built-in guidance for memory, monitoring, and automation ensures consistent orchestration outcomes across swarm topologies.

When should I use swarm orchestration instead of single-agent automation?▼

Swarm orchestration is needed when coordinating distributed experiments, automated task orchestration, and reproducible workflows across multiple agents. Use it when scalable mesh, hierarchical, star, or ring topologies are required to manage complex research, development, and testing workflows that exceed single-agent automation capabilities.