Swift Actor Persistence logo

Swift Actor Persistence

CommunityPopular
xu-xiang
swift-actor-persistence

在 Swift 中使用 actor 实现线程安全的数据持久化——基于内存缓存与文件支持的存储,通过设计消除数据竞争。

Overview

Publisherxu-xiang
Repositoryeverything-claude-code-zh
Skill nameswift-actor-persistence
Stars
2K
Forks
325
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by xu-xiang on GitHub. Read the source before you install it.

Installation

Install the Swift Actor Persistence AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/xu-xiang/everything-claude-code-zh/tree/main/docs/zh-CN/skills/swift-actor-persistence
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/xu-xiang/everything-claude-code-zh.git /tmp/everything-claude-code-zh
mkdir -p .claude/skills
cp -r /tmp/everything-claude-code-zh/docs/zh-CN/skills/swift-actor-persistence .claude/skills/swift-actor-persistence
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Swift Actor Persistence in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Swift Actor Persistence on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Swift Actor Persistence is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

用于线程安全持久化的 Swift Actor

使用 Swift actor 构建线程安全数据持久化层的模式。结合内存缓存与文件支持的存储,利用 actor 模型在编译时消除数据竞争。

何时激活

  • 在 Swift 5.5+ 中构建数据持久化层
  • 需要对共享可变状态进行线程安全访问
  • 希望消除手动同步(锁、DispatchQueue)
  • 构建具有本地存储的离线优先应用

核心模式

基于 Actor 的存储库

Actor 模型保证了序列化访问 —— 没有数据竞争,由编译器强制执行。

swift
public actor LocalRepository<T: Codable & Identifiable> where T.ID == String {
    private var cache: [String: T] = [:]
    private let fileURL: URL

    public init(directory: URL = .documentsDirectory, filename: String = "data.json") {
        self.fileURL = directory.appendingPathComponent(filename)
        // Synchronous load during init (actor isolation not yet active)
        self.cache = Self.loadSynchronously(from: fileURL)
    }

    // MARK: - Public API

    public func save(_ item: T) throws {
        cache[item.id] = item
        try persistToFile()
    }

    public func delete(_ id: String) throws {
        cache[id] = nil
        try persistToFile()
    }

    public func find(by id: String) -> T? {
        cache[id]
    }

    public func loadAll() -> [T] {
        Array(cache.values)
    }

    // MARK: - Private

    private func persistToFile() throws {
        let data = try JSONEncoder().encode(Array(cache.values))
        try data.write(to: fileURL, options: .atomic)
    }

    private static func loadSynchronously(from url: URL) -> [String: T] {
        guard let data = try? Data(contentsOf: url),
              let items = try? JSONDecoder().decode([T].self, from: data) else {
            return [:]
        }
        return Dictionary(uniqueKeysWithValues: items.map { ($0.id, $0) })
    }
}

用法

由于 actor 隔离,所有调用都会自动变为异步:

swift
let repository = LocalRepository<Question>()

// Read — fast O(1) lookup from in-memory cache
let question = await repository.find(by: "q-001")
let allQuestions = await repository.loadAll()

// Write — updates cache and persists to file atomically
try await repository.save(newQuestion)
try await repository.delete("q-001")

与 @Observable ViewModel 结合使用

swift
@Observable
final class QuestionListViewModel {
    private(set) var questions: [Question] = []
    private let repository: LocalRepository<Question>

    init(repository: LocalRepository<Question> = LocalRepository()) {
        self.repository = repository
    }

    func load() async {
        questions = await repository.loadAll()
    }

    func add(_ question: Question) async throws {
        try await repository.save(question)
        questions = await repository.loadAll()
    }
}

关键设计决策

决策理由
Actor(而非类 + 锁)编译器强制执行的线程安全性,无需手动同步
内存缓存 + 文件持久化从缓存中快速读取,持久化写入磁盘
同步初始化加载避免异步初始化的复杂性
按 ID 键控的字典按标识符进行 O(1) 查找
泛型化 Codable & Identifiable可在任何模型类型中重复使用
原子文件写入 (.atomic)防止崩溃时部分写入

最佳实践

  • 对所有跨越 actor 边界的数据使用 Sendable 类型
  • 保持 actor 的公共 API 最小化 —— 仅暴露领域操作,而非持久化细节
  • 使用 .atomic 写入 以防止应用在写入过程中崩溃导致数据损坏
  • 在 init 中同步加载 —— 异步初始化器会增加复杂性,而对本地文件的益处微乎其微
  • 与 @Observable ViewModel 结合使用 以实现响应式 UI 更新

应避免的反模式

  • 在 Swift 并发新代码中使用 DispatchQueue 或 NSLock 而非 actor
  • 将内部缓存字典暴露给外部调用者
  • 在不进行验证的情况下使文件 URL 可配置
  • 忘记所有 actor 方法调用都是 await —— 调用者必须处理异步上下文
  • 使用 nonisolated 来绕过 actor 隔离(违背了初衷)

何时使用

  • iOS/macOS 应用中的本地数据存储(用户数据、设置、缓存内容)
  • 稍后同步到服务器的离线优先架构
  • 应用中多个部分并发访问的任何共享可变状态
  • 用现代 Swift 并发性替换基于 DispatchQueue 的旧式线程安全机制

Frequently asked questions

What does the Swift Actor Persistence AI skill do?

在 Swift 中使用 actor 实现线程安全的数据持久化——基于内存缓存与文件支持的存储,通过设计消除数据竞争。

Why use Swift Actor Persistence on TypingMind?

Because you install it once and use it with any model. Swift Actor Persistence is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Swift Actor Persistence in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/xu-xiang/everything-claude-code-zh/tree/main/docs/zh-CN/skills/swift-actor-persistence. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Swift Actor Persistence?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Swift Actor Persistence?

As many as you like. As long as a model supports skills, you can use Swift Actor Persistence with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Swift Actor Persistence AI skill free?

Yes. It is published on GitHub by xu-xiang under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇