Cursor Usage

Key Features

As an advanced developer and Cursor power user, I’ll highlight three core features exclusive to Cursor that create a significant gap compared to other IDEs (like VS Code + Copilot). Mastering these features will accelerate your development speed by over five times.

1. Composer & Agent Mode (Cmd + I)

This goes beyond simply suggesting code; it enables system-wide design across multiple files.

Key Difference: While regular chat might say “Fix this file,” Composer can handle “Create and connect the API, DB schema, and frontend components needed for a new feature.”
Advanced Tip: Use Agent Mode. The AI executes terminal commands itself to install libraries, fixes errors autonomously, and loops through tasks until completion.

2. Codebase Indexing & @Symbol

Cursor indexes your entire project by vectorizing it. Even with hundreds of files, the AI knows all the connections.

Key Difference: Using @Codebase enables the AI to understand “where this data flows from and to across the entire project.”
Advanced Tip: When asking questions, narrow the scope using @Files or @Folders to maximize inference speed and accuracy.

3. AI Review (Beta)

After writing code, AI proactively suggests bugs or improvements.

Key Difference: Before you ask, AI will proactively say things like “A memory leak may occur here” or “This logic differs from the design defined in @docs.”
Advanced Tip: Always check the Review tab in the sidebar.

Development Flow

Here’s how to maximize Cursor’s AI performance while applying it directly to the server.

Step 1: Generate code locally and validate immediately

Cursor Chat: “Write a POST router in Hono to handle this Articles schema.”
Action: Apply the code and test locally at localhost:8787 while keeping pnpm wrangler dev open in the terminal.

Step 2: Instruct AI to Deploy

After local testing, command Cursor.
Prompt: “Logic verification complete. Now run wrangler deploy to deploy it to the server, then test using the deployed URL.”

Step 3: Remote Log Monitoring (Tail)

After deploying to the server, if errors occur, enter the following in the Cursor terminal:

pnpm wrangler tail

Debug: After enabling Cursor to read the remote error logs displayed in the terminal, instruct it: “Analyze these error logs, identify the cause, and fix it.”

.cursorrules configuration

To prevent AI errors during direct server deployment development, add the following rule to .cursorrules.

1. Cloudflare Workers AI 기능을 테스트할 때는 반드시 'wrangler deploy' 후 실제 환경에서 검증할 것.
2. 배포 전 반드시 'wrangler types'를 실행하여 D1이나 AI 바인딩의 타입을 최신화할 것.
3. 에러 발생 시 'wrangler tail' 로그를 분석하여 원격 환경의 제약 사항을 파악할 것.

Context Optimization (Advanced)

Focus on quality over quantity for the 85.9K / 128K token context.

Plan First, Code Second

Use Planning Mode: Describe tasks in detail after Plan:
Document Plans in .md: For complex tasks, document separately in Markdown then reference @파일

Surgical Context Injection

@파일이름: Tag only core files directly relevant to the task
@파일이름:줄번호: Specify only specific functions/code blocks
Remove Unrelated Files: Immediately remove automatically included irrelevant files

Chat Session Lifecycle

Cmd + L: New chat tab for new tasks
/summarize: Summarize and start new session when conversation gets long
Single Task: One chat = one clear task

Best Practice

“Using wrangler dev as the default, but frequently pushing critical features to the server” is the most robust approach. Especially with Modal.com integration, accurate testing locally can be difficult due to varying network environments, so deploying to the server in parallel is good for your mental health.