Doubao + Cursor in Practice: AI-Powered Web Scraper Development in One Minute — Full Workflow

Introduction

Web scraper development used to be a technical task requiring a solid programming foundation, but AI coding tools are dramatically lowering the barrier to entry. Recently, a Bilibili content creator demonstrated a highly efficient workflow: using Doubao to generate structured prompts, then having Cursor automatically generate a complete web scraper project — all in just one minute.

This combination of "AI-generated prompts + AI coding tool execution" is becoming an important method for developers to boost productivity.

Core Workflow: Three Steps from Requirements to Finished Product

Step 1: Generate Structured Prompts with Doubao

The starting point of the entire workflow isn't writing code directly — it's first leveraging Doubao (ByteDance's AI assistant) to generate a set of structured prompts. The key here is letting AI help you describe your scraping requirements more precisely and completely, avoiding gaps during subsequent code generation.

Structured prompting is a core methodology in Prompt Engineering. Unlike casually typing natural language, structured prompts guide AI to produce more accurate outputs through clearly defined modules — such as role definitions, task descriptions, output format requirements, and constraints. In a web scraping scenario, a good structured prompt typically needs to include the target URL, data fields to extract, data storage format, error handling requirements, and more. The essence of this approach is transforming vague human requirements into clear instructions that AI can execute efficiently.

This idea of "using AI to help write prompts" is worth paying attention to — when you're unsure how to describe your development requirements to Cursor, using a general-purpose AI like Doubao to organize your thoughts often leads to more accurate code output from the coding AI.

Step 2: One-Click Full-Stack Code Generation in Cursor

Copy the prompt generated by Doubao into Cursor and execute it. The AI will automatically generate a complete project with both frontend and backend code.

Cursor is an AI-native code editor built as a deep modification of VS Code, developed by Anysphere. Its core capability comes from deeply integrating large language models (such as GPT-4 and Claude) into the coding workflow, supporting code generation, modification, and debugging through natural language conversation. Unlike traditional code completion tools (such as GitHub Copilot), Cursor can understand the context of an entire project and perform cross-file code generation and refactoring. Its Agent mode can autonomously execute multi-step tasks, including creating files, installing dependencies, and running terminal commands — this is the technical foundation that enables it to generate full-stack projects in one go.

Cursor has a standout advantage in web scraper development scenarios: after generating code, it automatically creates a project summary document. This document includes:

• Backend functionality descriptions and API endpoint documentation
• Frontend interface functionality descriptions
• Detailed descriptions of all functional modules
• Complete project startup instructions

This means even programming beginners can follow the documentation step by step to deploy and run the scraper project.

Step 3: Launch and Verify According to the Documentation

Following the documentation auto-generated by Cursor, you first start the backend service, then launch the frontend interface. The entire process is as simple as following a tutorial. From startup to verifying the scraping results, it truly takes only about one minute.

Value and Considerations for AI-Powered Scraper Development

Why This AI Collaboration Model Works

1. Clear division of labor: Doubao handles requirements analysis and prompt optimization, while Cursor handles code generation and project scaffolding
2. Documentation-driven: Auto-generated operational documentation lowers the barrier to understanding and deployment
3. Full-stack generation: It doesn't just generate backend scraping logic — the frontend data display interface is also produced, ready to use out of the box

Issues to Watch for in Practice

While the demo results are impressive, there are important considerations when developing scrapers in real-world scenarios:

• Scraper compliance: You must comply with the target website's robots protocol and relevant laws and regulations. The robots protocol (robots.txt) is an industry standard where websites use a standard text file to inform crawlers which pages are allowed to be scraped and which are off-limits, first proposed in 1994. While it doesn't carry legal enforcement power, it's considered basic etiquette in the internet community. On the legal front, China's Data Security Law, Personal Information Protection Law, and Anti-Unfair Competition Law impose clear constraints on scraping activities. Large-scale unauthorized data scraping may violate criminal law provisions regarding "illegally obtaining computer information system data." Developers must assess compliance risks before using AI-generated scraper code.

• Code quality review: AI-generated scraper code may have inadequate handling of edge cases

• Anti-scraping countermeasures: Anti-scraping strategies in real production environments are far more complex than demo scenarios. Modern websites have developed multi-layered defense architectures: the basic layer includes User-Agent detection, IP rate limiting, and request header validation; the intermediate layer involves CAPTCHAs (image CAPTCHAs, slider verification, behavioral CAPTCHAs), Cookie encryption, and dynamic Token mechanisms; the advanced layer employs JavaScript obfuscation rendering (requiring browser automation tools like Selenium or Playwright), fingerprinting, and honeypot traps. AI-generated scraper code can typically only handle basic-layer protections — complex anti-scraping strategies still require human intervention.

• Ongoing maintenance costs: When the target website's structure changes, the scraper's parsing logic needs to be readjusted

AI Chain Collaboration: A New Development Paradigm

This case demonstrates more than just scraper development itself — it showcases a development paradigm worth watching: AI chain collaboration. Using the output of one AI tool as the input for another, completing complex development tasks through a combination of tools in a chain.

The concept of AI chain collaboration originates from the "Chain" concept proposed by frameworks like LangChain — linking multiple AI calls in logical sequence, where the output of one step automatically becomes the input for the next. This philosophy is permeating from the development framework level into everyday tool usage. Similar collaboration patterns include: using ChatGPT to generate requirements documents and then importing them into Figma AI to generate design mockups, or using Claude to analyze data and then passing the conclusions to Gamma to automatically generate presentation slides. This inter-tool collaboration is reshaping the division of labor in software development, evolving from "humans operating tools" to "humans orchestrating AI tool chains."

For developers who want to try this approach, here are the recommended steps:

1. First, clearly define the target website and the scope of data to be scraped
2. Use a general-purpose AI (such as Doubao or ChatGPT) to organize requirements and generate a structured prompt
3. Execute the prompt in an AI coding tool like Cursor to obtain the initial scraper code
4. Debug and optimize based on actual runtime results

This AI-assisted development approach is particularly well-suited for rapid prototype validation and small-scale scraper projects. However, for production environments, developers still need to manually review the generated code and perform targeted optimizations.