Passing Apple's Account Deletion Review in One Night with Cursor: A Complete Hands-On Walkthrough

Background: Apple's Data Compliance Requirement for Account Deletion

After submitting his app to the App Store, developer Lao Huang received a review response from Apple requiring the addition of an "account deletion" feature. This is a mandatory requirement under Apple's App data compliance policy — if you allow users to register, you must equally allow them to delete their accounts.

This policy isn't new. Apple officially enforced App Store Review Guideline Section 5.1.1(v) starting June 30, 2022, requiring all apps that support account creation to provide an account deletion feature. This policy stems from increasingly strict data privacy regulations worldwide, including the EU's GDPR (General Data Protection Regulation) "right to be forgotten" and California's CCPA (California Consumer Privacy Act). Apple requires the deletion feature to be easy to find (not buried deep in multiple menu layers) and to genuinely delete users' personal data rather than merely deactivating the account. Apps violating this requirement will be rejected during review, and already-published apps may be removed in subsequent reviews.

The requirement came suddenly, but Lao Huang decided to use Cursor to quickly implement the feature and documented the entire process. This case effectively demonstrates the efficiency gains AI programming tools bring to real-world iOS development scenarios.

Complete Workflow for Implementing iOS Account Deletion with Cursor

Writing Precise Prompts

Lao Huang entered a structured prompt in Cursor with these core requirements:

• Need to implement account deletion to meet Apple's data compliance requirements
• Reference best practices for similar features on iOS
• Add a delete account button in the user profile sheet view
• Make the deletion feature easily accessible to users
• Use Supabase's MCP (as the database) if needed

Supabase, mentioned here, is an open-source Firebase alternative built on PostgreSQL, offering Backend-as-a-Service (BaaS) capabilities including database, authentication, real-time subscriptions, storage, and edge functions. For indie developers and small teams, Supabase's advantages include using mature PostgreSQL rather than NoSQL under the hood, supporting complex queries and relational data modeling; providing Row Level Security (RLS) for database-level access control; and a built-in Auth module supporting multiple login methods. In the account deletion scenario, Supabase's Auth service provides admin-level user deletion APIs, and combined with RLS policies, ensures secure data removal.

A key detail here: Lao Huang had already configured comprehensive Rules files in the project, defining architectural standards like Clean Architecture and MVVM. This means Cursor-generated code automatically follows the project's architectural standards.

Some additional context on these architectural patterns: Clean Architecture is a software architecture philosophy proposed by Robert C. Martin. Its core idea is dividing code into concentric layers — the innermost layers are Entities and Use Cases representing business logic; outer layers are interface adapters and the framework layer. Dependencies can only point inward, ensuring business logic doesn't depend on specific UI frameworks or database implementations. MVVM (Model-View-ViewModel) is a UI-layer design pattern particularly popular in iOS development — Model handles data, View handles presentation, and ViewModel serves as the intermediary processing business logic and driving UI updates through data binding. When used together, MVVM typically serves as the implementation approach for Clean Architecture's presentation layer, making code highly testable and maintainable.

How Cursor Rules Files Work

Cursor Rules is a project-level AI behavior configuration mechanism provided by Cursor IDE. Developers can create a .cursorrules file in the project root (or create multiple rule files in the .cursor/rules directory), describing the project's tech stack, coding standards, architectural constraints, and best practices in natural language. When AI generates code, these rules are injected as system-level context into every conversation, ensuring generated code maintains consistent style and meets team standards. For example, you can specify "all network requests must go through the Repository layer," "use async/await instead of callbacks," or "error handling must use the Result type." This is essentially giving the AI a permanently active coding standards manual.

The AI's Execution Process

After clicking run, the Claude model began planning execution steps:

1. Searching best practices: Based on the prompt requirements, searching for iOS account deletion implementation approaches
2. Scanning the codebase: Understanding the current project's user authentication approach. Since each new command runs on a fresh basis, the AI needs to rescan the entire codebase
3. Implementing the feature: Adding deletion methods and repository methods in the auth feature

Lao Huang specifically mentioned a key insight: when the codebase is large, you need to write extensive documentation to help the AI understand the project structure more quickly. This is an important technique for using Cursor in large-scale project development.

Soft Delete Mechanism Design

The final implementation adopted a "soft delete" mechanism — a very professional design choice:

• After the user taps delete account, a confirmation dialog appears
• Upon confirmation, it displays "Deleting account data" then returns to the main screen
• The account enters a 30-day grace period rather than being permanently deleted immediately
• Within 30 days, users can log back in and choose to restore their account
• Upon restoration, cloud data transitions from "pending deletion" status back to normal

From a technical implementation perspective, soft delete is a common pattern in database design, contrasting with hard delete (directly removing records from the database). It's typically implemented by adding a marker field to the data table (such as a deleted_at timestamp or is_deleted boolean). When a user requests deletion, only this marker is updated without actually removing the data row. Queries filter out marked-as-deleted records through WHERE conditions. Advantages of this approach include: supporting data recovery, maintaining audit trails, avoiding foreign key constraint conflicts, and meeting regulatory data retention requirements. In account deletion scenarios, it's typically paired with scheduled tasks (Cron Jobs) that perform actual data purging after the grace period ends, including personal information anonymization or physical deletion.

This design satisfies Apple's compliance requirements while giving users room to change their minds — it's an industry-standard approach.

Testing, Verification, and Submission for Review

Since account deletion involves user data and is a sensitive feature, Lao Huang conducted extensive testing after implementation. From implementation on Monday to completing testing and packaging on Tuesday, the entire flow was run through many times. Only after confirming there were no issues with data operations did he decide to submit for review.

This reflects an important principle: AI can help you write code quickly, but testing and verification remain the developer's core responsibility. Especially for features involving user data, you can't skip thorough testing just because AI generated the code. For the account deletion feature, key paths to verify include: whether user data is correctly marked after deletion, whether logging back in during the grace period properly restores the account, whether the scheduled task correctly executes physical deletion after 30 days, and whether edge cases (such as network interruption during deletion) are handled robustly.

Cursor Usage Insights Distilled from Practice

Rules Files Are Key to Efficiency

Lao Huang's high efficiency largely stems from pre-configured Cursor Rules. These rules defined:

• Code architecture standards (Clean Architecture)
• Design pattern requirements (MVVM, etc.)
• Adherence to Apple best practices

With these rules in place, every new feature development maintains code quality and consistency.

Large Projects Need Documentation Support

As the codebase grows, the AI needs to re-understand project context each time. Writing clear documentation can significantly reduce the AI's "cognitive cost" and improve generated code accuracy. In practice, effective documentation strategies include: placing README files in key module directories, maintaining an Architecture Decision Record (ADR), and adding detailed comments at complex business logic points. This documentation not only helps AI understand the project but also serves as infrastructure for team collaboration.

MCP Integration Extends AI Capability Boundaries

Lao Huang used Supabase's MCP to let Cursor directly operate the database, significantly expanding the AI's capability range.

MCP (Model Context Protocol) is an open standard protocol released by Anthropic in late 2024, designed to provide AI models with a unified interface for interacting with external tools and data sources. It uses a client-server architecture: AI applications act as MCP clients initiating requests, while MCP servers encapsulate access capabilities to specific services (such as databases, APIs, file systems). With Supabase MCP integrated into Cursor, the AI can directly query database schemas, execute SQL, manage user authentication, and more — without developers manually copying and pasting database structure information. This greatly reduces context switching and enables AI to generate more accurate code based on actual data structures.

Lao Huang also plans to package the quantitative tool Qlib as an MCP, enabling AI to directly invoke quantitative analysis capabilities.

Summary and Outlook

This case demonstrates a real development scenario: facing a sudden compliance requirement, the complete workflow from requirements analysis, code implementation, to testing and verification was accomplished in one night with Cursor. The key success factors weren't just the AI tool itself, but the developer's upfront investment in Rules, documentation, and architecture.

Lao Huang revealed he'll next focus on tool development, particularly turning Qlib into an MCP. Qlib is an AI quantitative investment platform open-sourced by Microsoft Research Asia, providing a full-pipeline toolchain from data processing, factor mining, model training, to backtesting and trading. It includes multiple built-in machine learning models (such as LightGBM, LSTM, Transformer, etc.) for stock prediction and supports custom factor and strategy development. Packaging Qlib as an MCP service means AI programming assistants can directly invoke quantitative analysis capabilities — for example, having AI help write factor expressions, run backtests, and analyze strategy performance without developers manually writing complex data processing pipelines. This "AI calling AI tools" pattern represents the evolutionary direction of development toolchains.

He believes the quantitative field currently lacks good AI toolchains, making this a valuable direction. For developers interested in personal quantitative trading, a quantitative tool that AI can directly invoke would significantly lower the barrier to entry.

Key Takeaways

• Apple's compliance requirement: if you allow registration, you must allow account deletion — developers need to implement this feature
• Pre-configuring Rules files and project documentation enables Cursor to generate high-quality code that conforms to architectural standards
• The account deletion feature uses a soft delete mechanism with a 30-day grace period — both compliant and user-friendly
• After AI generates code, developers must still conduct thorough data security testing — verification cannot be skipped
• MCP integration (e.g., Supabase) significantly extends the capability boundaries of AI programming tools