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Domain Cloud Native

OrganizationPopular
actionbook
domain-cloud-native

Use when building cloud-native apps. Keywords: kubernetes, k8s, docker, container, grpc, tonic, microservice, service mesh, observability, tracing, metrics, health check, cloud, deployment, 云原生, 微服务, 容器

Overview

Publisheractionbook
Repositoryrust-skills
Skill namedomain-cloud-native
Stars
1.5K
Forks
114
Bundled files
Instructions only
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 actionbook on GitHub. Read the source before you install it.

Installation

Install the Domain Cloud Native 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/actionbook/rust-skills/tree/main/skills/domain-cloud-native
  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/actionbook/rust-skills.git /tmp/rust-skills
mkdir -p .claude/skills
cp -r /tmp/rust-skills/skills/domain-cloud-native .claude/skills/domain-cloud-native
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Domain Cloud Native 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 Domain Cloud Native 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 Domain Cloud Native 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.

Cloud-Native Domain

Layer 3: Domain Constraints

Domain Constraints → Design Implications

Domain RuleDesign ConstraintRust Implication
12-FactorConfig from envEnvironment-based config
ObservabilityMetrics + tracestracing + opentelemetry
Health checksLiveness/readinessDedicated endpoints
Graceful shutdownClean terminationSignal handling
Horizontal scaleStateless designNo local state
Container-friendlySmall binariesRelease optimization

Critical Constraints

Stateless Design

RULE: No local persistent state
WHY: Pods can be killed/rescheduled anytime
RUST: External state (Redis, DB), no static mut

Graceful Shutdown

RULE: Handle SIGTERM, drain connections
WHY: Zero-downtime deployments
RUST: tokio::signal + graceful shutdown

Observability

RULE: Every request must be traceable
WHY: Debugging distributed systems
RUST: tracing spans, opentelemetry export

Trace Down ↓

From constraints to design (Layer 2):

"Need distributed tracing"
    ↓ m12-lifecycle: Span lifecycle
    ↓ tracing + opentelemetry

"Need graceful shutdown"
    ↓ m07-concurrency: Signal handling
    ↓ m12-lifecycle: Connection draining

"Need health checks"
    ↓ domain-web: HTTP endpoints
    ↓ m06-error-handling: Health status

Key Crates

PurposeCrate
gRPCtonic
Kuberneteskube, kube-runtime
Dockerbollard
Tracingtracing, opentelemetry
Metricsprometheus, metrics
Configconfig, figment
HealthHTTP endpoints

Design Patterns

PatternPurposeImplementation
gRPC servicesService meshtonic + tower
K8s operatorsCustom resourceskube-runtime Controller
ObservabilityDebuggingtracing + OTEL
Health checksOrchestration/health, /ready
Config12-factorEnv vars + secrets

Code Pattern: Graceful Shutdown

rust
use tokio::signal;

async fn run_server() -> anyhow::Result<()> {
    let app = Router::new()
        .route("/health", get(health))
        .route("/ready", get(ready));

    let addr = SocketAddr::from(([0, 0, 0, 0], 8080));

    axum::Server::bind(&addr)
        .serve(app.into_make_service())
        .with_graceful_shutdown(shutdown_signal())
        .await?;

    Ok(())
}

async fn shutdown_signal() {
    signal::ctrl_c().await.expect("failed to listen for ctrl+c");
    tracing::info!("shutdown signal received");
}

Health Check Pattern

rust
async fn health() -> StatusCode {
    StatusCode::OK
}

async fn ready(State(db): State<Arc<DbPool>>) -> StatusCode {
    match db.ping().await {
        Ok(_) => StatusCode::OK,
        Err(_) => StatusCode::SERVICE_UNAVAILABLE,
    }
}

Common Mistakes

MistakeDomain ViolationFix
Local file stateNot statelessExternal storage
No SIGTERM handlingHard killsGraceful shutdown
No tracingCan't debugtracing spans
Static configNot 12-factorEnv vars

Trace to Layer 1

ConstraintLayer 2 PatternLayer 1 Implementation
StatelessExternal stateArc for external
Graceful shutdownSignal handlingtokio::signal
TracingSpan lifecycletracing + OTEL
Health checksHTTP endpointsDedicated routes

Related Skills

WhenSee
Async patternsm07-concurrency
HTTP endpointsdomain-web
Error handlingm13-domain-error
Resource lifecyclem12-lifecycle

Frequently asked questions

What does the Domain Cloud Native AI skill do?

Use when building cloud-native apps. Keywords: kubernetes, k8s, docker, container, grpc, tonic, microservice, service mesh, observability, tracing, metrics, health check, cloud, deployment, 云原生, 微服务, 容器

Why use Domain Cloud Native on TypingMind?

Because you install it once and use it with any model. Domain Cloud Native 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 Domain Cloud Native in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/actionbook/rust-skills/tree/main/skills/domain-cloud-native. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Domain Cloud Native?

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 Domain Cloud Native?

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

Is the Domain Cloud Native AI skill free?

It is published on GitHub by actionbook. Check the repository for licensing terms. 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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