domain-cloud-native▌
zhanghandong/rust-skills · updated Apr 8, 2026
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Design constraints and patterns for building stateless, observable cloud-native applications in Rust.
- ›Enforces stateless design, graceful shutdown with SIGTERM handling, and 12-factor configuration via environment variables to support Kubernetes orchestration and zero-downtime deployments
- ›Requires distributed tracing with tracing and OpenTelemetry, plus dedicated /health and /ready endpoints for liveness and readiness probes
- ›Recommends key crates: tonic for gRPC services, kube and ku
Cloud-Native Domain
Layer 3: Domain Constraints
Domain Constraints → Design Implications
| Domain Rule | Design Constraint | Rust Implication |
|---|---|---|
| 12-Factor | Config from env | Environment-based config |
| Observability | Metrics + traces | tracing + opentelemetry |
| Health checks | Liveness/readiness | Dedicated endpoints |
| Graceful shutdown | Clean termination | Signal handling |
| Horizontal scale | Stateless design | No local state |
| Container-friendly | Small binaries | Release 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
| Purpose | Crate |
|---|---|
| gRPC | tonic |
| Kubernetes | kube, kube-runtime |
| Docker | bollard |
| Tracing | tracing, opentelemetry |
| Metrics | prometheus, metrics |
| Config | config, figment |
| Health | HTTP endpoints |
Design Patterns
| Pattern | Purpose | Implementation |
|---|---|---|
| gRPC services | Service mesh | tonic + tower |
| K8s operators | Custom resources | kube-runtime Controller |
| Observability | Debugging | tracing + OTEL |
| Health checks | Orchestration | /health, /ready |
| Config | 12-factor | Env vars + secrets |
Code Pattern: Graceful Shutdown
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
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
| Mistake | Domain Violation | Fix |
|---|---|---|
| Local file state | Not stateless | External storage |
| No SIGTERM handling | Hard kills | Graceful shutdown |
| No tracing | Can't debug | tracing spans |
| Static config | Not 12-factor | Env vars |
Trace to Layer 1
| Constraint | Layer 2 Pattern | Layer 1 Implementation |
|---|---|---|
| Stateless | External state | Arc for external |
| Graceful shutdown | Signal handling | tokio::signal |
| Tracing | Span lifecycle | tracing + OTEL |
| Health checks | HTTP endpoints | Dedicated routes |
Related Skills
| When | See |
|---|---|
| Async patterns | m07-concurrency |
| HTTP endpoints | domain-web |
| Error handling | m13-domain-error |
| Resource lifecycle | m12-lifecycle |
How to use domain-cloud-native on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add domain-cloud-native
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches domain-cloud-native from GitHub repository zhanghandong/rust-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate domain-cloud-native. Access the skill through slash commands (e.g., /domain-cloud-native) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.5★★★★★43 reviews- ★★★★★Dhruvi Jain· Dec 28, 2024
Solid pick for teams standardizing on skills: domain-cloud-native is focused, and the summary matches what you get after install.
- ★★★★★Harper Abbas· Dec 28, 2024
Registry listing for domain-cloud-native matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Advait Thompson· Dec 8, 2024
domain-cloud-native fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Advait Gupta· Nov 27, 2024
domain-cloud-native has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Oshnikdeep· Nov 19, 2024
We added domain-cloud-native from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Aditi Okafor· Nov 19, 2024
Useful defaults in domain-cloud-native — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Advait Shah· Oct 18, 2024
Solid pick for teams standardizing on skills: domain-cloud-native is focused, and the summary matches what you get after install.
- ★★★★★Ganesh Mohane· Oct 10, 2024
domain-cloud-native fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Harper Ramirez· Oct 10, 2024
I recommend domain-cloud-native for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Chen Kapoor· Sep 13, 2024
Useful defaults in domain-cloud-native — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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