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swarm-advanced

Orchestrates multi-agent swarms for research, development, testing, and analysis workflows.

Updated Mar 25, 2026
Search Tags:#parallel execution#multi-agent#mcp#orchestration#swarm#distributed workflows#claude-flow
One-click install
npx skills add https://github.com/dmuhoro/EasyTutor --skill swarm-advanced-dmuhoro
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: swarm-advanced
Source: https://github.com/dmuhoro/EasyTutor/tree/main/archive/.claude/skills/swarm-advanced
Command: npx skills add https://github.com/dmuhoro/EasyTutor --skill swarm-advanced-dmuhoro

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires claude-flow.

What problem does it solve? Coordinating multiple AI agents across complex tasks like research, full-stack development, and testing requires manual orchestration, which is error-prone and hard to scale. This Skill provides structured swarm patterns with defined topologies, agent roles, and workflows. ## Core Features & Use Cases - Swarm Topologies: Choose mesh, hierarchical, star, or ring topologies matched to research, development, testing, or pipeline workflows. - Prebuilt Patterns: Ready-to-use research, development, testing, and analysis swarm architectures with phased workflows and parallel execution. - Fault Tolerance & Memory: Error recovery, state snapshots, namespaced memory, and neural pattern learning for cross-session persistence. - Use Case: Spin up a hierarchical development swarm with an architect, backend and frontend developers, testers, and a reviewer to design, implement, test, and deploy a REST API in coordinated phases. ## Quick Start Initialize a mesh swarm with six agents and orchestrate a parallel research task on a topic of your choice using the Claude Flow MCP tools.

Frequently Asked Questions about swarm-advanced

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

FAQPage Schema
How do I set up a multi-agent swarm with Claude Flow?▼

Install claude-flow via npm, add the MCP server, then call swarm_init with a topology like mesh or hierarchical. Spawn agents with agent_spawn and coordinate work using task_orchestrate or parallel_execute.

Which swarm topology should I use for my workflow?▼

Use mesh for research and collaborative analysis, hierarchical for structured development, star for centralized testing, and ring for sequential pipeline processing. The choice depends on whether tasks need peer communication, command structure, or staged execution.

Can I run a swarm without MCP tools using the CLI?▼

Yes, each pattern includes a CLI fallback using npx claude-flow swarm with flags like --strategy, --mode, --max-agents, and --parallel. This runs the same orchestration without configuring the MCP server.

How does the swarm handle errors and agent failures?▼

The skill configures fault tolerance with auto-recovery strategies, checks swarm health via swarm_status, and analyzes failures with error_analysis. Failed tasks can be retried sequentially after verifying the swarm is healthy.

Does swarm state persist across sessions?▼

Yes, memory can be persisted with memory_persist, organized into namespaces with TTL values, and checkpointed using state_snapshot. Snapshots can be restored later with context_restore, and backups can be written to disk.

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