CodeBuddy Best Practices Guide - Progressive Learning Path ​

From zero foundation to advanced practices, systematically master CodeBuddy AI programming skills

Learning Path Overview ​

This guide follows the principle of progressive learning, dividing CodeBuddy best practices into four levels to help developers of different skill levels gradually master AI programming skills.

text
Basic Introduction → Advanced Techniques → Engineering Practices → Advanced Applications

Level 1: Basic Introduction ​

Target Audience: AI programming beginners, CodeBuddy newcomers Learning Objectives: Master basic operations, establish AI programming mindset

1.1 Quick Start Practice ​

  • Building a Personal Blog Website from 0 to 1

    • Core Value: Complete project with zero coding foundation
    • Practice Highlights: Plan Mode usage, project planning mindset
    • Suitable Scenarios: Complete beginners who want to quickly experience AI development

    Detailed Practice Operations: Click "Building a Personal Blog Website from 0 to 1" for more details

    Step 1: Requirements Analysis and Planning

    • Open CodeBuddy, select Plan Mode
    • Describe blog requirements: "I want to create a personal tech blog with article display, category management, and comment functionality"

      Task Description

    • AI automatically generates project structure: Frontend (React/Vue), Backend (Node.js), Database (MongoDB)
    • Confirm tech stack selection and functional module division

    Step 2: Rapid Prototype Development

    • Use AI to generate project scaffolding and basic code structure
    • Implement core functional modules: homepage, article list, article details, admin backend
    • Integrate modern UI component libraries (such as Ant Design or Material-UI)
    • Add responsive design and basic styling

Step 3: Feature Enhancement and Optimization

  • Implement CRUD operations for articles and category management
  • Add search functionality and tag system
  • Integrate Markdown editor and code highlighting
  • Implement comment system and user interaction features

Step 4: Deployment and Launch

  • Use CloudStudio or CloudBase for one-click deployment

    • Create a todo web application

    Deployment Success

    * Deploy todo web application to CloudStudio

    Deployment Success

    • Create a todo web application Link

    Deployment Success

  • Configure custom domain and HTTPS certificate

  • Set up CDN acceleration and performance optimization

  • Obtain accessible online blog address

    Deployment Integration

1.2 Product Validation and Prototyping ​

  • How to Quickly Generate Demonstrable Prototypes

    • Core Value: Quickly validate product concepts, reduce trial and error costs
    • Practice Highlights: MVP mindset, prototype design, user validation
    • Suitable Scenarios: Startup teams, product managers validating ideas and concepts

    Detailed Practice Operations: Click "How to Quickly Generate Demonstrable Prototypes" for more details

    Step 1: GENIUS for MVP Framework Application

    Core Validation Three Questions:

    • What is the core hypothesis we want to validate? (Rather than "building a product")
    • What is the minimum evidence to validate this hypothesis? (User behavior, feedback, willingness to pay)
    • To what extent does the prototype need to be built to obtain evidence? (Real backend vs fake data)

    MVP Three-Layer Pyramid:

    • Concept Layer: Can users understand the value proposition and find it "interesting"
    • Process Layer: Can they smoothly complete 1-2 core tasks in the prototype
    • Value Layer: Are they willing to pay a price for it (time, data, money)

    Step 2: Efficient Construction Pipeline

    Technical Implementation Path:

    • Rapid Skeleton Generation: Use tools like v0, Bolt.new to build basic framework in 30 minutes
    • Local Enhancement Optimization: Use CodeBuddy to improve skeleton interaction and API integration
    • Template Reuse: Precipitate successful patterns into reusable MVP templates

    Actual Case: Travel Vertical MVP

    • Original idea: "Help young people plan weekend short trips"
    • User research revealed real needs: avoid being cheated, avoid pitfalls, avoid hassle
    • Converged hypothesis: "One-click generation of travel plans with real reviews + bookable links"
    • 2-hour Spec: Single homepage entry, three-module plan, 3 cities support, payment redirection

    Step 3: Spec-Driven MVP Development

    Lightweight Spec Includes:

    • Background and Goals: Solve whose what problem
    • Core User Stories: "As XX, I hope to XX, so I don't have to XX"
    • Golden Path Process: Simple description of Step 1/2/3
    • Acceptance Criteria: What behavior counts as "this iteration complete"

    Development Chain:

    • Vibe Plan: AI upgrades ideas to structured requirements and user stories
    • Vibe Design: Automatically generate wireframes/high-fidelity interfaces based on requirements
    • Vibe Coding: Generate code under Spec constraints, avoid "feeling-based" development
    • Vibe Deploy: One-click deployment and launch in CodeBuddy

    Step 4: Rapid Iteration Learning Loop

    User Signal Capture:

    • Strong Behavioral Signals: Active sharing, recommending to friends, multiple uses
    • Strong Text Signals: Serious feedback, not just "okay, pretty good"
    • Strong Emotional Signals: "Wow, this works" vs "This doesn't help me"

    AI-Assisted Analysis:

    • Automatically summarize user behavior patterns
    • Cluster and organize feedback opinions
    • Extract 3-5 "next most important things to change"
    • Form "Spec → Prototype → Data → New Spec" closed loop

1.3 Modern Development Paradigms ​

  • Vibe Coding Development Best Practices: Building Modern Login Systems

    • Core Value: Master visual-first modern frontend development philosophy
    • Practice Highlights: Component-based development, parameterized theming, fluid interactions
    • Suitable Scenarios: Frontend developers improving UI/UX design and implementation capabilities

    Detailed Practice Operations: Click "Vibe Coding Development Best Practices: Building Modern Login Systems" for more details

    Step 1: Establish Vibe Coding Design Thinking

    Core Concepts:

    • Visual First: Establish visual style (lighting, texture, color) before writing logic
    • Fluid Interactions: UI provides delicate feedback to user operations
    • AI Synergy: Use CodeBuddy AI to quickly generate boilerplate code

    Design Style Selection: Glassmorphism Tech Blue UI

    • Glassmorphism design style combined with tech blue tones
    • Semi-transparent effects and soft shadows create layered spatial sense
    • Modern, lightweight, and professional visual experience

    Step 2: Built-in Component Ecosystem Rapid Development

    UI Component Ecosystem

    Tech Stack Configuration:

    json
    {
  "Framework": "React 18 (TypeScript)",
  "Build Tool": "Vite", 
  "UI Component Library": "shadcn/ui",
  "Styling Solution": "Tailwind CSS",
  "Form Handling": "React Hook Form",
  "Form Validation": "Zod"
}

    shadcn/ui Component Library Advantages:

    • Avoid reinventing the wheel with basic UI components
    • Directly call high-quality components that have been design-polished
    • Ensure accessibility and interaction details
    • Focus on business logic and "Vibe" adjustments

    Step 3: Parameterized Theming System Implementation

    10-Second Theming Core Technology:

    • Modify Tailwind color class names for quick theming
    • Change from bg-indigo-500 to bg-emerald-500
    • Login page transforms from "cyber tech style" to "natural forest style"
    • No need to touch core interaction logic or animation curves

    Practical Application Scenarios:

    • Adapt to new product brand colors, avoid rewriting large amounts of CSS
    • Avoid style conflicts, improve development efficiency
    • Support multi-theme switching, meet different user preferences

    Step 4: Standardized Premium Micro-interactions

    Core Feature Implementation:

    • Email Password Login: Real-time input validation and friendly error prompts
    • Third-party OAuth Login: Seamless GitHub and Google redirection
    • User Experience Optimization: "Remember me" option, forgot password, loading state feedback
    • Responsive Design: Consistent experience on desktop and mobile

    Micro-interaction Details:

    • Input field breathing effects
    • Button streaming animations
    • Form validation real-time feedback
    • Loading state smooth transitions

    Interaction Baseline Standardization:

    • Build Vibe templates with perfect micro-interactions
    • Become company interaction baseline, ensure consistent experience across product lines
    • Reduce design restoration communication costs
    • Developers of different levels can produce high-quality interactions

Level 2: Advanced Techniques ​

Target Audience: Developers with some programming foundation who want to improve efficiency Learning Objectives: Master efficient workflows, improve development efficiency

2.1 Efficiency Tool Mastery ​

Step 1: Identify Repetitive Task Scenarios

  • Daily Report Generation Scenario: Daily need to write work summaries and next-day plans
  • TAPD Requirement Creation: Frequently create requirement tickets, fill repetitive fields
  • Code Review Scenario: Check code for security, performance, and compliance
  • Todo Query: Cross-system TAPD todo queries, generate plans by priority

Step 2: Create Project-Level Custom Commands

Create Custom Command

  • Input / to call Create Command instruction
  • Define command name (e.g., kdy-report, tapd-todo, code-review)
  • Configure command MD file content
  • Input /command-name in dialog box for one-click trigger

Actual Command Example - Daily Report Generator:

markdown
---
description: "Ultra-fast daily report generation - Auto-generate today's work summary in 10 seconds"
aliases: ["daily", "standup", "report"]
---

# Ultra-fast Daily Report Generation

## Usage
Generate daily/weekly report
Write daily/weekly report
What did I do today/this week

Actual Command Example - TAPD Todo Query:

markdown
---
allowed-tools: Bash(curl:*), Bash(jq:*), Write, Read
description: Use TAPD MCP Server to query todos, generate priority plans
---

# TAPD Todo Query and Plan Generation Tool

## Workflow
1. Get data from TAPD system
   ├─ Pending stories
   ├─ Pending bugs  
   └─ Pending tasks

2. Categorize and sort by priority
   ├─ High (Urgent - 1-2 days)
   ├─ Middle (Medium - 3-5 days)
   └─ Low (Normal - 7+ days)

Step 3: Establish Command Library Classification System

  • Development Efficiency: Code generation, refactoring, testing, documentation
  • Project Management: Requirement creation, progress tracking, daily/weekly reports
  • Code Quality: Security review, performance optimization, compliance checking
  • Collaboration Communication: Meeting minutes, technical sharing, issue summaries

Step 4: Actual Usage Effect Validation

  • Daily Report Generation: From 15 minutes manual writing to 30 seconds AI generation
  • Requirement Creation: From 5 minutes form filling to 1 minute voice description
  • Code Review: Automated security, performance, compliance three-dimensional checking
  • Todo Management: Cross-system data integration, intelligent priority sorting

2.2 Reducing Context Token Consumption Practical Tips ​

  • CodeBuddy Efficient Context Management Tutorial and Practical Tips for Reducing Token Consumption

    • Core Value: Understand context window mechanisms, master cost control
    • Practice Highlights: Token billing principles, context management strategies
    • Suitable Scenarios: Developers concerned with cost control and efficiency optimization

    Detailed Practice Operations: Click "CodeBuddy Efficient Context Management Tutorial and Practical Tips for Reducing Token Consumption" for more details

    Step 1: Understanding Token Billing Mechanism

    Billing Model Analysis:

    • Input Tokens: All text you send to AI (including conversation history, file content, instructions)
    • Output Tokens: AI-generated response content
    • Context Window: Maximum number of tokens AI can "remember" (typically 32K-128K)
    • Cost Structure: Input tokens are cheap, output tokens are more expensive (about 3-5x price difference)

    Actual Cost Cases:

    • One complete code review: about 1000-3000 tokens
    • Long conversation (50 rounds): may consume 20K-50K tokens
    • Large file analysis: single file may be 10K+ tokens

    Step 2: Context Management Strategies

    Segmented Conversation Techniques:

    • Functional Module Separation: Use different conversation windows for different functions
    • Periodic Summarization: Actively summarize key information every 10-15 conversation rounds
    • Context Cleanup: Regularly start new conversations, keep only necessary information
    • File Reference Optimization: Only @ needed file fragments, avoid full-text references

    Smart Reference Methods:

    bash
    # Inefficient way
@entire project folder  # May introduce tens of thousands of tokens

# Efficient way  
@src/components/Login.tsx:1-50  # Only reference needed line numbers
@README.md # Only reference key documents

    Step 3: Long Conversation Optimization Techniques

    Structured Conversation Management:

    • Topic Marking: Use clear titles to distinguish different discussion topics
    • Decision Recording: Summarize important decisions in concise language
    • Code Snippet Management: Avoid repeatedly pasting the same code
    • Progressive Refinement: Discuss big framework first, then dive into details

    Practical Operation Tips:

    • Send only changed parts after each modification, not complete files
    • Use "based on previous discussion" rather than re-describing background
    • Make good use of CodeBuddy's file diff feature, focus only on changes
    • Regularly export important conversation content to avoid information loss

    Step 4: Cost Control Best Practices

    Daily Usage Habits:

    • Batch Processing: Combine similar problems to solve in one conversation
    • Template Reuse: Save common prompts as custom commands
    • Precise Questioning: Clear specific needs, avoid vague descriptions leading to multiple clarifications
    • Result Verification: Test in small scope first, confirm direction before large-scale application

    Team Collaboration Optimization:

    • Establish team-level best practice documentation
    • Share efficient prompt templates
    • Regularly review token usage, optimize workflows
    • Train team members to master cost control techniques

2.3 Cross-Domain Applications ​

  • Everyone Can Learn CodeBuddy PPT Writing Guide

    • Core Value: Efficient application of AI in non-programming scenarios
    • Practice Highlights: Document generation, presentation creation, content structuring
    • Suitable Scenarios: Product, operations, marketing personnel who need to create presentation documents

    Detailed Practice Operations: Click "Everyone Can Learn CodeBuddy PPT Writing Guide" for more details

    Step 1: Preparation - Installing CodeBuddy

    Currently, CodeBuddy IDE & CodeBuddy Code CLI and Plugin all support Skills. The following uses CodeBuddy IDE as an example:

    • Official website: /
    • Operation manual: /docs/ide/Quick%20Start

    Step 2: Skill Package Installation and Initial Experience

    Since we need to write PPT, we need to install Skills packages with presentation creation, editing, and analysis capabilities. Currently, there are official Skills packages from Anthropics on GitHub, where the document-skills package contains 4 Skill configurations for docx, ppt, pdf, xlsx, meeting the needs for creating, editing, and analyzing Word, PPT, PDF, Excel file types.

    Installing document-skills package in CodeBuddy IDE (for non-professional developers):

    • Download installation package locally and double-click to extract
    • Open CodeBuddy IDE, create local folder

      Create Local Folder

    • Enter configuration page, find Skill configuration path (user Skill is global configuration, configure once and use in any folder)

    Create Local Folder

    • Import user-level Skill
    • Check if configuration is successful
    • (Optional) Import xlsx, pdf, docx Skills for installation, fully covering office scenarios

    Initial Skills Experience:

    In CodeBuddy dialog box, select Agent mode, select model, input the following Prompt:

    text
    For non-professional developers, based on  website content,
write an introduction PPT, background color using Tencent Blue #0161FF, name PPT as Tencent Blue Version

    Operation Guide:

    Interact with CodeBuddy Agent

    Effect Example:

    Step 3: PPT Content Framework Construction

    AI-Assisted Outline Generation:

    • Scenario Description: Describe PPT goals, audience, duration, core information to AI in detail
    • Structured Output: Require AI to generate three-level structure of "title-subtitle-points"
    • Logic Check: Ensure content logic is clear, well-layered, and highlights are prominent
    • Time Allocation: Reasonably allocate content proportion for each part based on presentation duration

    Practical Operation Example:

    text
    I need to create a 20-minute technical sharing PPT, theme is "Landing Practice of AI Programming Tools in Teams",
audience is company's technical team, hoping to share our team's experience and effects using CodeBuddy.
Please help me generate a complete PPT outline structure.

    Step 4: Content Generation and Optimization

    Page Content Creation:

    • Title Page: Eye-catching main title and subtitle design
    • Table of Contents: Clear chapter navigation and expected gains
    • Content Pages: 3-5 points per page, avoid information overload
    • Summary Page: Core viewpoint review and action recommendations

    Copy Quality Enhancement:

    • Data Support: Use specific numbers and cases to enhance persuasiveness
    • Storytelling: Package boring data into vivid stories
    • Golden Sentence Refinement: Refine 1-2 memorable points for each chapter
    • Audience Language: Use terminology and expressions familiar to target audience

    Step 5: Skills Feature Template Production

    PPT Skills Toolkit:

    • Outline Generator: Input theme and requirements, automatically generate structured outline
    • Content Expander: Expand key points into complete page content
    • Chart Advisor: Recommend suitable visualization methods based on data types
    • Speech Generator: Convert PPT content into speeches and notes

    Template Library Construction:

    • Report Templates: Work summaries, project progress, achievement displays
    • Training Templates: Technical sharing, best practices, experience transfer
    • Proposal Templates: Project initiation, resource requests, solution comparisons
    • Marketing Templates: Product introductions, customer demonstrations, business proposals

    Step 6: Multimedia Content AI-Assisted Creation

    Visual Element Design:

    • Color Schemes: Generate color suggestions based on theme and brand requirements
    • Icon Selection: Match appropriate icons and symbols for different concepts
    • Layout Design: Optimize page layout, enhance visual hierarchy
    • Animation Effects: Design appropriate page transitions and element animations

    Data Visualization:

    • Chart Type Selection: Bar charts, line charts, pie charts, scatter plots, etc.
    • Data Cleaning: Organize and format raw data
    • Trend Analysis: Mine stories and insights behind data
    • Interactive Design: Add interactive explanations for complex charts

    Practical Application Tips:

    • Use AI-generated content as first draft, then manually refine
    • Maintain brand consistency, ensure visual style meets company standards
    • Adjust content depth and expression for different audiences
    • Reserve interactive sessions to enhance presentation effect and audience participation

Level 3: Engineering Practices ​

Target Audience: Team leaders, architects, developers hoping to scale AI applications Learning Objectives: Master team collaboration, engineering practices

3.1 Team Collaboration and Standards ​

Step 1: Establish Spec Standard System

  • Define requirement description templates: feature overview, user stories, acceptance criteria
  • Create technical specification formats: API design, data models, architecture diagrams
  • Establish code standards: naming conventions, comment standards, code structure
  • Formulate review processes: Spec review, technical review, acceptance testing

Step 2: Spec-Kit Tool Practice

  • Install and configure Spec-Kit toolchain
  • Create project templates: automatically generate project structure based on Spec
  • Implement Spec parsing: convert requirement documents into executable code
  • Integrate CI/CD: automated testing, deployment, documentation generation

Step 3: Spec-Driven Development Process

  • Requirements analysis phase: Product managers write functional Spec
  • Design phase: Architects complete technical Spec and API design
  • Development phase: Developers code implementation based on Spec
  • Testing phase: QA writes test cases based on Spec
  • Acceptance phase: Functional acceptance against Spec

Step 4: Quality Assurance Mechanism

  • Code review: Spec-based code quality checking
  • Automated testing: unit testing, integration testing, end-to-end testing
  • Documentation synchronization: code changes automatically update Spec documents
  • Continuous improvement: collect feedback, optimize Spec standards and processes

3.2 Enterprise-Level Project Practice ​

  • Rapidly Building Enterprise CRM: Achieving One-Stop Frontend and Backend Development and Deployment with CodeBuddy

    • Core Value: Complete enterprise application development process
    • Practice Highlights: Full-stack development, database design, deployment and operations
    • Suitable Scenarios: Development teams needing to rapidly deliver enterprise applications

    Detailed Practice Operations: Click "Rapidly Building Enterprise CRM: Achieving One-Stop Frontend and Backend Development and Deployment with CodeBuddy" for more details

    Step 1: CloudBase Cloud Development Environment Setup

    Environment Configuration: Cloud database, cloud functions, cloud storage, cloud hosting

    Development Toolchain: CodeBuddy IDE + CloudBase CLI, Git version control, debugging monitoring, CI/CD deployment

    Step 2: AI-Assisted Data Model Design

    CRM Core Entities: Customer management, sales management, product management, user permissions

    Database Design Practice:

    javascript
    // AI-generated customer entity model
const CustomerSchema = {
  id: String,
  name: String,
  company: String,
  contacts: [{
    type: String, // phone, email, wechat
    value: String,
    isPrimary: Boolean
  }],
  tags: [String],
  status: String, // lead, prospect, customer, inactive
  assignedTo: String, // assigned sales ID
  createdAt: Date,
  updatedAt: Date
}

    Step 3: Frontend-Backend Separation Architecture Implementation

    Backend API: RESTful design, JWT authentication, data validation, unified error handling

    Frontend Architecture: React/Vue + TypeScript, state management, route permissions, component reuse

    Step 4: DevOps Automated Deployment

    One-Click Deployment: Code commit → Automated testing → Build packaging → Environment deployment

    Monitoring and Operations: Performance monitoring, error monitoring, business monitoring, security monitoring

3.3 Enterprise Scenario Landing Practice ​

  • CodeBuddy AI Coding Enterprise Scenario Landing Practice and Reflections

    • Core Value: Systematic enterprise-level AI Coding landing methodology
    • Practice Highlights: Team diagnosis, tool selection, process improvement, effect evaluation
    • Suitable Scenarios: Enterprise technical managers, team leaders promoting AI Coding landing

    Detailed Practice Operations: Click "CodeBuddy AI Coding Enterprise Scenario Landing Practice and Reflections" for more details

    Step 1: Team Development Process Diagnosis

    • Draw complete team development process diagram (requirements analysis → development → testing → deployment → operations)
    • Identify pain points for each role: Product managers (frequent requirement changes), developers (repetitive work), testers (time-consuming regression testing)
    • Quantitative analysis: code writing time ratio, bug fixing time, deployment frequency and other key indicators
    • Evaluate team AI acceptance and tech stack compatibility

    Step 2: AI Coding Tool Selection Framework

    • Technical compatibility assessment: existing tech stack support, learning costs, integration difficulty
    • Team acceptance research: understand team attitudes toward AI tools through surveys and interviews
    • ROI calculation model: expected efficiency improvement vs tool costs and training investment
    • Develop tool selection matrix: feature coverage, usability, cost, technical support

    Step 3: Progressive Landing Strategy

    • Individual Phase: Select 1-2 technical leaders to try first, accumulate success cases
    • Group Phase: Promote within one project group, establish best practice templates
    • Team Phase: Team-wide promotion, establish training system and support mechanisms
    • Organization Phase: Cross-department promotion, establish enterprise-level AI Coding standards

    Step 4: Effect Evaluation and Continuous Optimization

    • Establish key indicator system: development efficiency improvement, code quality improvement, bug reduction rate
    • Regularly collect feedback: monthly team reviews, quarterly effect evaluations
    • Continuously optimize processes: adjust tool configurations and usage methods based on data feedback
    • Knowledge precipitation: establish enterprise-level AI Coding knowledge base and best practice documentation

Level 4: Advanced Applications ​

Target Audience: Technical experts, innovation practitioners Learning Objectives: Explore cutting-edge applications of AI programming

4.1 Capability Extension and Ecosystem ​

  • AI Programming Practice Driven by CodeBuddy + Skills

    • Core Value: Build professional domain AI capability systems
    • Practice Highlights: Skills development, capability assembly, knowledge precipitation
    • Suitable Scenarios: Technical experts and teams hoping to build professional capability systems

    Detailed Practice Operations: Click "AI Programming Practice Driven by CodeBuddy + Skills" for more details

    Step 1: Understanding Skills Essence and Value

    Skills Core Concept: Transform expert experience into reusable AI capabilities, achieving domain specialization and capability modularization

    Application Scenarios: Code review, documentation generation, test cases, deployment and operations

    Step 2: Professional Domain Skills Development

    Skills Development Process:

    1. Requirements Analysis: Identify team tasks with high repetition and strong professionalism
    2. Knowledge Extraction: Collect expert experience, best practices, standard processes
    3. Capability Design: Define input/output, processing logic, exception handling
    4. Testing Validation: Validate Skills effectiveness and accuracy in real scenarios

    Technical Implementation Methods:

    python
    # Skills Example: Code Quality Check
class CodeQualitySkill:
    def analyze_code(self, code, language, focus_areas):
        """
        Code Quality Analysis Skills
        Input: Code snippet, programming language, focus areas
        Output: Issue list, improvement suggestions, scoring
        """
        results = {
            'security_issues': self.check_security(code),
            'performance_issues': self.check_performance(code),
            'style_issues': self.check_style(code),
            'suggestions': self.generate_suggestions(code)
        }
        return results

    Step 3: Skills Ecosystem Construction and Assembly

    Capability Assembly Modes: Serial, parallel, conditional, loop assembly

    Ecosystem Management: Unified repository, capability catalog, usage statistics, continuous optimization

    Step 4: Knowledge Precipitation and Capability Inheritance

    Extract project experience into reusable methodologies, achieve new employee training, expert experience inheritance, and cross-team collaboration through Skills

4.2 Automated Testing and MCP ​

  • Efficiency Revolution: CodeBuddy + Chrome MCP Reshaping E-commerce Development and Automated Testing New Paradigm

    • Core Value: AI-driven end-to-end test automation
    • Practice Highlights: MCP protocol, automated testing, AI agents
    • Suitable Scenarios: Technical teams pursuing ultimate efficiency and automation

    Detailed Practice Operations: Click "Efficiency Revolution: CodeBuddy + Chrome MCP Reshaping E-commerce Development and Automated Testing New Paradigm" for more details

    Step 1: AI Rapid E-commerce Website Construction

    E-commerce Core Functions: Product display, shopping cart, user system, payment process

    Development Strategy: Use mature templates, component reuse, third-party API integration, responsive design

    Step 2: Chrome DevTools MCP Integration

    Understanding MCP Protocol:

    • Model Context Protocol: Standardized communication protocol between AI and browser
    • Core Capabilities: Page operations, element location, network monitoring, performance analysis
    • Intelligent Interaction: AI understands page structure, automatically generates operation sequences

    Technical Architecture Design:

    javascript
    // Chrome MCP Connection Example
const chromeMCP = new ChromeMCPClient({
  endpoint: 'ws://localhost:8080/mcp',
  capabilities: [
    'page_navigation',
    'element_interaction', 
    'network_monitoring',
    'performance_analysis'
  ]
});

// AI-Driven Test Cases
const testCase = {
  name: 'User Purchase Process Test',
  steps: [
    { action: 'navigate', url: 'https://shop.example.com' },
    { action: 'click', selector: '.product-item:first-child' },
    { action: 'click', selector: '.add-to-cart-btn' },
    { action: 'verify', condition: 'cart_count > 0' }
  ]
};

    Step 3: AI Agent-Driven End-to-End Testing

    Intelligent Testing Process:

    • AI generates test scenarios and analyzes page structure
    • Automatically execute test steps, simulate user behavior
    • Intelligently analyze results and identify anomalies

    Test Coverage:

    • Comprehensive testing of functionality, interface, performance, compatibility
    • Intelligent retry, error location, screenshot recording

    Step 4: Complete Automation Closed Loop from Development to Validation

    Continuous Integration Process: Code commit → Automated build deployment → AI test execution → Result feedback

    Effect Improvement:

    • Development cycle shortened by 50%+, test coverage reaches 80%+
    • Bug fixing speed improved by 3x, reduced manual testing costs

Practice Case Details ​

Typical Practice Case Showcase ​

Based on the above best practices guide, we provide detailed practice cases showing the complete learning path from zero foundation to advanced applications. Each case includes:

  • Complete Operation Steps: Detailed practical operation guidance
  • Core Technical Points: In-depth analysis of key technologies and methods
  • Actual Effect Validation: Quantifiable effect data and achievement display
  • Related Resource Links: Supporting tutorial documents and code examples

More Learning Resources ​

Detailed tutorial documents can be viewed by clicking corresponding article links, and welcome to follow the "Tencent Cloud Code Assistant CodeBuddy" official account for more information.

Welcome everyone to use CodeBuddy and explore best practices!