open-agent-builder

🥇Gold

The open-agent-builder is a powerful visual workflow tool for creating AI agents with ease. It features drag-and-drop functionality for building web scraping pipelines, enabling real-time execution and seamless integration into your projects.

2,0983920Updated Today

Intermediate15 minutes to implementdevelopment

Saves ~150 min per use

Quick InstallView Source

claude install firecrawl/open-agent-builder

Works with:

ChatGPTGitHub Copilot

Overview

About This Skill

#documentation

Use Cases

Build dynamic web scraping pipelines to gather market intelligence.

Automate data collection processes for lead generation campaigns.

Create complex workflows for competitive analysis and reporting.

Integrate real-time data extraction into existing applications.

Best For

GrowthRevOps

Setup & Installation

Quick Install

Terminal

claude install firecrawl/open-agent-builder

Alternative Install (Git Clone)

git clone https://github.com/firecrawl/open-agent-builder

Requirements

Claude Code or compatible AI agent
Works with: ChatGPT, GitHub Copilot

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 web scraping pipeline for [COMPANY] in the [INDUSTRY] sector. The pipeline should extract [DATA] from [WEBSITE] and save it to a CSV file. Include error handling for network issues and rate limiting.

Example Output

# Web Scraping Pipeline for Retail Inventory Tracking

## Pipeline Overview

This pipeline scrapes product inventory data from e-commerce websites for retail analysis.

### Inputs

- Target URL: `https://www.example-retailer.com/products`
- Data fields: Product name, price, stock status
- Rate limit: 2 requests per second

### Outputs

- CSV file: `retail_inventory.csv`
- Error log: `scraping_errors.log`

### Pipeline Steps

1. **Initialization**: Set up HTTP client with headers and rate limiter
2. **Request Handling**: Fetch product pages with retry logic
3. **Data Extraction**: Parse HTML to extract product details
4. **Data Processing**: Clean and format extracted data
5. **Output Handling**: Write to CSV and log errors
6. **Monitoring**: Track pipeline performance metrics