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Http Cache

Community
rshankras
http-cache

Generates an HTTP caching layer with Cache-Control parsing, ETag/conditional requests, and offline fallback. Use when user wants to add response caching, offline support, or reduce API calls.

Overview

Publisherrshankras
Repositoryclaude-code-apple-skills
Skill namehttp-cache
Stars
744
Forks
70
Bundled files
2
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.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by rshankras on GitHub. Read the source before you install it.

Installation

Install the Http Cache 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.
  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/rshankras/claude-code-apple-skills.git /tmp/claude-code-apple-skills
mkdir -p .claude/skills
cp -r /tmp/claude-code-apple-skills/skills/generators/http-cache .claude/skills/http-cache
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Http Cache 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 Http Cache 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 Http Cache 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.

HTTP Cache Generator

Generate a production HTTP caching layer that integrates with your existing networking code. Supports Cache-Control directives, ETag/Last-Modified conditional requests, stale-while-revalidate, and offline fallback.

When This Skill Activates

Use this skill when the user:

  • Asks to "add HTTP caching" or "cache API responses"
  • Wants "offline support" or "offline fallback"
  • Mentions "reduce API calls" or "cache network responses"
  • Asks about "ETag" or "conditional requests" or "304 Not Modified"
  • Wants "stale-while-revalidate" behavior

Pre-Generation Checks

1. Project Context Detection

  • Check Swift version (requires Swift 5.9+)
  • Check deployment target (iOS 16+ / macOS 13+)
  • Search for existing caching implementations
  • Identify source file locations

2. Networking Layer Detection

Search for existing networking code:

Glob: **/*API*.swift, **/*Client*.swift, **/*Network*.swift
Grep: "APIClient" or "URLSession" or "HTTPURLResponse"

If networking-layer generator was used, detect the APIClient protocol and generate a decorator that wraps it.

3. Conflict Detection

Search for existing caching:

Glob: **/*Cache*.swift
Grep: "URLCache" or "ResponseCache" or "CachePolicy"

If found, ask user whether to replace or extend.

Configuration Questions

Ask user via AskUserQuestion:

  1. Cache storage sizes?

    • Small (10 MB memory / 50 MB disk)
    • Medium (25 MB memory / 100 MB disk) — recommended
    • Large (50 MB memory / 250 MB disk)
  2. Caching strategy?

    • Respect server Cache-Control headers (standard)
    • Cache-first with background revalidation (stale-while-revalidate)
    • Manual per-endpoint policies
  3. Offline support?

    • Yes — serve stale cache when network unavailable
    • No — only cache while online
  4. Integration style?

    • Decorator wrapping existing APIClient (recommended if networking-layer exists)
    • Standalone cache you call directly

Generation Process

Step 1: Read Templates

Read http-cache-patterns.md for architecture guidance. Read templates.md for production Swift code.

Step 2: Create Core Files

Generate these files:

  1. HTTPCacheConfiguration.swift — Memory/disk sizes, default policy
  2. CachePolicy.swift — Per-endpoint enum (default, noCache, forceCache, cacheFirst)
  3. CacheControlHeader.swift — Cache-Control header parser
  4. ConditionalRequestHandler.swift — ETag/Last-Modified/304 handling
  5. HTTPResponseCache.swift — Protocol + disk-backed response store

Step 3: Create Integration Files

Based on configuration:

  • CachingAPIClient.swift — Decorator wrapping existing APIClient (if decorator style)
  • NetworkReachability.swift — NWPathMonitor wrapper (if offline support selected)

Step 4: Determine File Location

Check project structure:

  • If Sources/Networking/ exists → Sources/Networking/Cache/
  • If App/Networking/ exists → App/Networking/Cache/
  • If Networking/ exists → Networking/Cache/
  • Otherwise → Cache/

Output Format

After generation, provide:

Files Created

Networking/Cache/
├── HTTPCacheConfiguration.swift    # Memory/disk sizes, default policy
├── CachePolicy.swift               # Per-endpoint caching enum
├── CacheControlHeader.swift        # Cache-Control header parser
├── ConditionalRequestHandler.swift # ETag/Last-Modified/304
├── HTTPResponseCache.swift         # Protocol + disk implementation
├── CachingAPIClient.swift          # Decorator for existing APIClient
└── NetworkReachability.swift       # NWPathMonitor (optional)

Integration Steps

Wrap your existing client:

swift
let baseClient = URLSessionAPIClient(configuration: .production)
let cachingClient = CachingAPIClient(
    wrapping: baseClient,
    cache: DiskHTTPResponseCache(),
    configuration: .default
)

// Use cachingClient everywhere you used baseClient
let users = try await cachingClient.request(UsersEndpoint())

Per-endpoint cache policy:

swift
struct UsersEndpoint: APIEndpoint, CacheConfigurable {
    var cachePolicy: CachePolicy { .cacheFirst(maxAge: 300) }
}

struct OrdersEndpoint: APIEndpoint, CacheConfigurable {
    var cachePolicy: CachePolicy { .noCache }
}

With SwiftUI:

swift
struct UsersView: View {
    @Environment(\.apiClient) private var apiClient

    var body: some View {
        List(users) { user in
            Text(user.name)
        }
        .task {
            // Automatically uses cache if available
            users = try await apiClient.request(UsersEndpoint())
        }
    }
}

Testing

swift
@Test
func cachedResponseReturnedOnSecondRequest() async throws {
    let mockClient = MockAPIClient()
    let cache = InMemoryHTTPResponseCache()
    let cachingClient = CachingAPIClient(wrapping: mockClient, cache: cache)

    mockClient.mockResponse(for: UsersEndpoint.self, response: [.mock])

    // First request hits network
    let first = try await cachingClient.request(UsersEndpoint())
    #expect(mockClient.requestCount == 1)

    // Second request comes from cache
    let second = try await cachingClient.request(UsersEndpoint())
    #expect(mockClient.requestCount == 1) // No additional network call
    #expect(first == second)
}

References

  • http-cache-patterns.md — Cache-Control directives, ETag flow, stale-while-revalidate
  • templates.md — All production Swift templates
  • Related: generators/networking-layer — Base networking layer this decorates

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Http Cache AI skill do?

Generates an HTTP caching layer with Cache-Control parsing, ETag/conditional requests, and offline fallback. Use when user wants to add response caching, offline support, or reduce API calls.

Why use Http Cache on TypingMind?

Because you install it once and use it with any model. Http Cache 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 Http Cache in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rshankras/claude-code-apple-skills/tree/main/skills/generators/http-cache. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Http Cache?

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 Http Cache?

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

Is the Http Cache AI skill free?

Yes. It is published on GitHub by rshankras 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.

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