Issue #117

Hello everyone!

After building SyntaxKit with AI assistance, I tackled rebuilding MistKit—my CloudKit library frozen since 2021. Using Claude Code and swift-openapi-generator, I transformed Apple's 10-year-old CloudKit REST API docs into 10,476 lines of type-safe Swift. Here's what I learned from 428 sessions over three months.

Rebuilding MistKit: From Documentation to Production

MistKit had been frozen since 2021, missing out on Swift 6, async/await, and modern server-side patterns. I saw an opportunity: if Claude Code could translate SwiftSyntax ASTs into a DSL, it could translate CloudKit's REST API documentation into an OpenAPI specification.

The Technical Solution:

• Polymorphic Types: OpenAPI's oneOf pattern with CustomFieldValue handling CloudKit's dynamic field types
• Authentication: ClientMiddleware supporting API tokens, web auth, and server-to-server methods
• Error Handling: Comprehensive OpenAPI schema with type-safe error codes and recovery guidance
• Architecture: Three-layer design with generated client (internal), abstraction layer (internal), and clean public API

The Development Workflow:

Using llm.codes, I converted Apple's documentation into markdown and stored it in .claude/docs/, with a CLAUDE.md file acting as a table of contents. This gave Claude persistent context without repeated prompting—essential since its training predates tools like swift-openapi-generator. I provided CloudKit knowledge and architecture, Claude generated schemas and patterns. Built CLI tools (Celestra, Bushel) to validate beyond unit tests. 3 months of side-project work vs 6-12 months solo.

What Works and What Doesn't:

Claude Code excels at:

• Test Generation: Created 4,161 comprehensive tests covering all edge cases
• Pattern Application: Once shown a pattern, applies it consistently across the codebase
• Refactoring at Scale: 200K token context window enables updating dozens of files simultaneously

Claude Code struggles with:

• Architectural Vision: Requires human guidance for system design and holistic decisions
• Boundary Management: "Grabby AI" would reference internal OpenAPI types instead of abstraction layer
• Newer APIs: Training data predates tools like Swift Testing—documentation is essential

The key insight: AI is a force multiplier, not a replacement. I provided vision and domain knowledge, Claude handled implementation and mechanical transformations.

Read the complete story in Part 1 (technical details) and Part 2 (real-world lessons).

Get Started with MistKit

MistKit v1.0 Alpha is available as an open-source Swift package with working Celestra and Bushel CLI examples. Built using swift-openapi-generator for type-safe CloudKit access.

Quick Example:

// Add to Package.swift
dependencies: [
.package(url: "https://github.com/brightdigit/MistKit.git", 
from: "1.0.0-alpha.3")
]
// Server-to-Server Authentication
let service = try CloudKitService(
container: "iCloud.com.example.MyApp",
environment: .production,
database: .public,
tokenManager: ServerToServerTokenManager(
keyID: keyID,
privateKey: privateKey
)
)
// Type-safe operations
let records = try await service.queryRecords(
recordType: "Article",
filter: .greaterThan("publishDate", .date(cutoff))
)

Resources:

• Repository: github.com/brightdigit/MistKit
• Documentation: Complete API Reference
• Read the Full Series:
  • Part 1: From CloudKit Docs to Type-Safe Swift
  • Part 2: Real-World Lessons and Collaboration Patterns
• Examples: Celestra and Bushel CLI Tools

I'd love to hear about your experiences with AI-assisted development and CloudKit projects.

Happy building!

Leo