swarm

Name: swarm
Availability: InStock
Author: SoftSpark

Distribute complex tasks across multiple autonomous agents using map-reduce, consensus, or relay modes.

161|21|Updated Mar 26, 2026

Search Tags:#agent #consensus #parallel #swarm #relay #map-reduce

Ready to run

npx skills add https://github.com/softspark/ai-toolkit --skill swarm-softspark

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: swarm
Source: https://github.com/softspark/ai-toolkit/tree/main/app/skills/swarm
Command: npx skills add https://github.com/softspark/ai-toolkit --skill swarm-softspark

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Manually coordinating multiple autonomous agents is time‑consuming and risk‑prone; teams need a reliable way to run tasks in parallel while guaranteeing distinct file ownership and seamless result aggregation.

Core Features & Use Cases

Map‑Reduce Mode: Split a complex task into independent sub‑tasks and launch all agents simultaneously for parallel execution.
Consensus Mode: Deploy three specialized agents to approach a problem from different angles and select the highest‑confidence answer.
Relay Mode: Chain agents sequentially, passing each agent's output to the next for step‑wise refinement.
File Ownership Enforcement: Guarantees each agent works on exclusive file paths, preventing overlap and conflicts.
Aggregation Process: De‑duplicates, synthesizes, and produces a final swarm report after all agents complete.

Quick Start

Run /swarm map-reduce build documentation to launch parallel agents that each handle a portion of the documentation task.

Frequently Asked Questions about swarm

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

FAQPage Schema

How do I run parallel agents to speed up complex software development tasks?▼

You can run parallel agents by using map-reduce mode to split a complex task into independent sub-tasks and launching multiple agents simultaneously. This distributes the workload across autonomous agents to achieve faster execution and reliable results.

What is the best way to prevent file conflicts when executing autonomous agents simultaneously?▼

To prevent file conflicts during simultaneous agent execution, the system enforces strict non-overlapping file ownership per agent. This guarantees each agent works on exclusive file paths, eliminating overlap and merge conflicts during parallel workflows.

When should I use consensus mode versus relay mode for multi-agent orchestration?▼

Use consensus mode to deploy three specialized agents that approach a problem from different angles and select the highest-confidence answer. Use relay mode to chain agents sequentially, passing each output to the next for step-wise refinement.

Do I need the Agent tool to distribute map-reduce workflows across autonomous agents?▼

Yes, the Agent tool is required to distribute map-reduce workflows across autonomous agents. It enables the parallel execution and orchestration needed to split complex tasks into independent sub-tasks for simultaneous processing.

How does parallel task aggregation work after multiple agents complete their execution?▼

Parallel task aggregation works by de-duplicating and synthesizing the outputs from all completed agents. The system produces a final swarm report that consolidates the individual results into a unified outcome after execution finishes.

Can I use parallel agents for architecture design and testing workflows?▼

Yes, you can use parallel agents for architecture design and testing workflows. The orchestration modes support software development processes by distributing complex tasks across autonomous agents to achieve faster results.