Easy Spider GuideSAFE

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---
name: easy-spider-guide
description: "Guide to EasySpider for visual no-code web data collection"
metadata:
  openclaw:
    emoji: "🕷️"
    category: "tools"
    subcategory: "scraping"
    keywords: ["web scraping", "visual crawler", "no-code scraping", "data collection", "research data", "web automation"]
    source: "https://github.com/NaiboWang/EasySpider"
---

# EasySpider Guide

## Overview

EasySpider is a visual, no-code web crawler tool with over 44K stars on GitHub. It provides a graphical interface where users design web scraping tasks by interacting directly with target web pages, clicking on elements to extract, and defining navigation flows visually. No programming knowledge is required to build functional scrapers, making it accessible to researchers across all disciplines.

For academic researchers, data collection from web sources is a frequent need but often a technical barrier. Whether gathering publication metadata from journal websites, collecting survey responses from public forums, extracting pricing data for economic research, or archiving web content for digital humanities projects, EasySpider enables researchers to build custom scrapers without writing Python or JavaScript code. The visual approach also makes scrapers easier to maintain and modify when target websites change their structure.

EasySpider runs as a desktop application on Windows, macOS, and Linux. It uses a built-in Chromium browser for rendering, which means it can handle JavaScript-heavy websites, single-page applications, and sites that require user interaction such as clicking buttons, scrolling, or filling forms. Scraped data can be exported as CSV, JSON, or directly to databases.

## Installation

### Download and Setup

```bash
# Download the latest release for your platform from GitHub releases
# https://github.com/NaiboWang/EasySpider/releases

# macOS - download the .dmg file and drag to Applications

# Linux - download the AppImage
chmod +x EasySpider-linux-x86_64.AppImage
./EasySpider-linux-x86_64.AppImage

# Or run from source
git clone https://github.com/NaiboWang/EasySpider.git
cd EasySpider
npm install
npm start
```

### System Requirements

- Operating system: Windows 10+, macOS 10.15+, or Linux (Ubuntu 18.04+)
- RAM: 4 GB minimum, 8 GB recommended for complex scraping tasks
- Disk space: 500 MB for the application plus storage for scraped data
- Network: stable internet connection for web scraping

## Core Concepts

### Task Design Workflow

EasySpider follows a visual task design approach with these steps:

1. **Open target page** - Enter the URL in EasySpider's built-in browser
2. **Select elements** - Click on the data elements you want to extract
3. **Define fields** - Name each extracted element (title, author, date, etc.)
4. **Configure pagination** - Click the "next page" button to set up pagination
5. **Set extraction rules** - Define how to handle lists, tables, and nested pages
6. **Test and run** - Preview results, then execute the full scraping task

### Element Selection Modes

- **Single element** - Click one element to extract that specific item
- **Similar elements** - Click two similar items and EasySpider detects the pattern for all matching elements on the page
- **Table mode** - Select a table header row to extract entire structured tables
- **Input mode** - Define form fields to fill before extracting (useful for search-based data collection)

## Research Use Cases

### Collecting Publication Metadata

Researchers can use EasySpider to gather publication information from journal websites, conference proceedings pages, or institutional repositories.

**Example workflow for scraping a conference proceedings page:**

1. Navigate to the proceedings listing page
2. Click on the first paper title to mark it as a "title" field
3. Click on the second paper title; EasySpider recognizes the pattern and selects all titles
4. Similarly select author names, abstract snippets, and publication dates
5. If papers span multiple pages, click the "Next" pagination button
6. Configure "click into each paper" to follow links and extract full abstracts
7. Run the task and export as CSV

### Monitoring Research Funding Opportunities

```
Task: Daily scan of funding agency websites for new opportunities

Steps configured in EasySpider:
1. Navigate to funding agency announcement page
2. Extract: opportunity title, deadline, funding amount, eligibility
3. Filter: only new announcements (since last check)
4. Schedule: run daily at 8:00 AM
5. Export: append to CSV file, send notification email
```

### Gathering Economic Data from Public Sources

For economics and social science research, EasySpider can collect publicly available data from government statistics portals, price comparison websites, and public registries.

```
Task: Collect commodity prices from public market websites

Fields to extract:
- commodity_name: product identifier
- price: current listed price
- unit: measurement unit
- date: listing date
- source_url: page URL for reference

Pagination: navigate through category pages
Schedule: weekly collection
Output: CSV with timestamp for time-series analysis
```

### Digital Humanities Web Archiving

```
Task: Archive public blog posts for discourse analysis

Configuration:
- Start URL: blog archive page
- Follow: links matching pattern /posts/*
- Extract per page:
  - post_title
  - post_date
  - author_name
  - post_content (full text)
  - comment_count
  - tags/categories
- Pagination: follow archive navigation links
- Output: JSON with full text content
```

## Advanced Features

### Conditional Logic

EasySpider supports conditional branches in task flows:

- **If element exists** - Check for specific elements before attempting extraction
- **If text contains** - Filter items based on content matching
- **Loop control** - Set maximum iterations for pagination or nested page visits

### Data Cleaning Options

Built-in text processing options can be applied during ex

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

What the source does

Filesystem

none-observed

Network

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Shell

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Dependencies

pinned

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Findings (0)

No findings outside the package's declared scope.

Gates applied: no_behavioural_pass.