claude-flow

🥇Gold

🌊 The leading agent orchestration platform for Claude. Deploy intelligent multi-agent swarms, coordinate autonomous workflows, and build conversational AI systems. Features enterprise-grade architecture, distributed swarm intelligence, RAG integration, and native Claude Code support via MCP protocol. Ranked #1 in agent-based frameworks.

14,4471,5910Updated Today

Intermediate30min to implementautomation

Saves ~360 min per use

Quick InstallView Source

git clone https://github.com/ruvnet/claude-flow.git

Works with:

Claude

Overview

About This Skill

claude-flow is a leading agent orchestration platform designed specifically for Claude, enabling users to deploy intelligent multi-agent swarms that coordinate autonomous workflows and build sophisticated conversational AI systems. With its enterprise-grade architecture and native Claude Code support via the MCP protocol, claude-flow stands out as the top choice in agent-based frameworks. Its advanced features, including distributed swarm intelligence and RAG integration, empower teams to automate complex tasks seamlessly. One of the key benefits of using claude-flow is its ability to significantly enhance workflow automation efficiency. By implementing this skill, teams can save valuable time through the automation of various processes. For instance, code reviews can be automated by deploying specialized agents that assess code quality and suggest improvements, leading to faster turnaround times and higher quality outputs. Additionally, coordinating testing workflows across multiple agents ensures comprehensive coverage and accelerates feedback loops, which is crucial for maintaining software integrity. This skill is particularly beneficial for developers, product managers, and AI practitioners who are looking to optimize their workflows and enhance productivity. It is ideal for teams involved in software development, DevOps, and AI system design. By utilizing claude-flow, organizations can implement security audits through dedicated agents that scan for vulnerabilities, facilitate documentation generation, and optimize deployment pipelines, thereby streamlining their entire development process. While the implementation of claude-flow is classified as advanced, it can typically be set up in about 30 minutes. Users should have a foundational understanding of AI automation and workflow management to maximize the skill's potential. As organizations increasingly adopt AI-first strategies, claude-flow integrates seamlessly into these workflows, allowing teams to harness the power of automation and focus on higher-value tasks.

Use Cases

Automate code reviews by deploying specialized agents that assess code quality and suggest improvements.

Coordinate testing workflows across multiple agents to ensure comprehensive coverage and faster feedback loops.

Implement security audits using dedicated agents that scan for vulnerabilities and compliance issues in software projects.

Facilitate documentation generation by utilizing agents that extract information from codebases and create user-friendly guides.

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/ruvnet/claude-flow

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Create a multi-agent workflow for [COMPANY] in the [INDUSTRY] using Claude-Flow. The workflow should include tasks such as [TASK1], [TASK2], and [TASK3]. Please provide a detailed structure and any necessary parameters.

Example Output

### Multi-Agent Workflow for Tech Innovations Inc.

**Industry:** Technology

**Workflow Overview:**
1. **Task 1: Data Collection**  
   - Agents gather user feedback from various platforms (surveys, social media).
   - Parameters: Collection frequency - daily; Target platforms - Twitter, Facebook, Company website.

2. **Task 2: Analysis**  
   - An AI agent analyzes feedback sentiment and categorizes it into positive, neutral, and negative.
   - Parameters: Analysis type - sentiment analysis; Tools used - NLP algorithms.

3. **Task 3: Reporting**  
   - Generate a report summarizing the findings and insights.
   - Parameters: Format - PDF; Frequency - weekly.

**Outcome:**  
The workflow automates the feedback loop, ensuring that Tech Innovations Inc. stays responsive to user needs while saving time and resources through intelligent automation.