domain-iot

zhanghandong/rust-skills · updated Apr 8, 2026

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$npx skills add https://github.com/zhanghandong/rust-skills --skill domain-iot
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summary

Design patterns and constraints for building reliable, power-efficient IoT applications in Rust.

  • Covers six critical domains: network unreliability, power constraints, resource limits, security, reliability, and over-the-air updates with specific Rust implementation strategies
  • Provides MQTT client patterns using rumqttc for pub/sub messaging with QoS levels, local buffering, and retry logic with exponential backoff
  • Distinguishes between Linux gateway stacks (tokio + std) and MCU devi
skill.md

IoT Domain

Layer 3: Domain Constraints

Domain Constraints → Design Implications

Domain Rule Design Constraint Rust Implication
Unreliable network Offline-first Local buffering
Power constraints Efficient code Sleep modes, minimal alloc
Resource limits Small footprint no_std where needed
Security Encrypted comms TLS, signed firmware
Reliability Self-recovery Watchdog, error handling
OTA updates Safe upgrades Rollback capability

Critical Constraints

Network Unreliability

RULE: Network can fail at any time
WHY: Wireless, remote locations
RUST: Local queue, retry with backoff

Power Management

RULE: Minimize power consumption
WHY: Battery life, energy costs
RUST: Sleep modes, efficient algorithms

Device Security

RULE: All communication encrypted
WHY: Physical access possible
RUST: TLS, signed messages

Trace Down ↓

From constraints to design (Layer 2):

"Need offline-first design"
    ↓ m12-lifecycle: Local buffer with persistence
    ↓ m13-domain-error: Retry with backoff

"Need power efficiency"
    ↓ domain-embedded: no_std patterns
    ↓ m10-performance: Minimal allocations

"Need reliable messaging"
    ↓ m07-concurrency: Async with timeout
    ↓ MQTT: QoS levels

Environment Comparison

Environment Stack Crates
Linux gateway tokio + std rumqttc, reqwest
MCU device embassy + no_std embedded-hal
Hybrid Split workloads Both

Key Crates

Purpose Crate
MQTT (std) rumqttc, paho-mqtt
Embedded embedded-hal, embassy
Async (std) tokio
Async (no_std) embassy
Logging (no_std) defmt
Logging (std) tracing

Design Patterns

Pattern Purpose Implementation
Pub/Sub Device comms MQTT topics
Edge compute Local processing Filter before upload
OTA updates Firmware upgrade Signed + rollback
Power mgmt Battery life Sleep + wake events
Store & forward Network reliability Local queue

Code Pattern: MQTT Client

use rumqttc::{AsyncClient, MqttOptions, QoS};

async fn run_mqtt() -> anyhow::Result<()> {
    let mut options = MqttOptions::new("device-1", "broker.example.com", 1883);
    options.set_keep_alive(Duration::from_secs(30));

    let (client, mut eventloop) = AsyncClient::new(options, 10);

    // Subscribe to commands
    client.subscribe("devices/device-1/commands", QoS::AtLeastOnce).await?;

    // Publish telemetry
    tokio::spawn(async move {
        loop {
            let data = read_sensor().await;
            client.publish("devices/device-1/telemetry", QoS::AtLeastOnce, false, data).await.ok();
            tokio::time::sleep(Duration::from_secs(60)).await;
        }
    });

    // Process events
    loop {
        match eventloop.poll().await {
            Ok(event) => handle_event(event).await,
            Err(e) => {
                tracing::error!("MQTT error: {}", e);
                tokio::time::sleep(Duration::from_secs(5)).await;
            }
        }
    }
}

Common Mistakes

Mistake Domain Violation Fix
No retry logic Lost data Exponential backoff
Always-on radio Battery drain Sleep between sends
Unencrypted MQTT Security risk TLS
No local buffer Network outage = data loss Persist locally

Trace to Layer 1

Constraint Layer 2 Pattern Layer 1 Implementation
Offline-first Store & forward Local queue + flush
Power efficiency Sleep patterns Timer-based wake
Network reliability Retry tokio-retry, backoff
Security TLS rustls, native-tls

Related Skills

When See
Embedded patterns domain-embedded
Async patterns m07-concurrency
Error recovery m13-domain-error
Performance m10-performance
how to use domain-iot

How to use domain-iot on Cursor

AI-first code editor with Composer

1

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-iot
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/zhanghandong/rust-skills --skill domain-iot

The skills CLI fetches domain-iot from GitHub repository zhanghandong/rust-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/domain-iot

Reload or restart Cursor to activate domain-iot. Access the skill through slash commands (e.g., /domain-iot) 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

GET_STARTED →

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. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 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

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.528 reviews
  • Pratham Ware· Dec 16, 2024

    domain-iot reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Sakshi Patil· Nov 7, 2024

    I recommend domain-iot for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Chaitanya Patil· Oct 26, 2024

    Useful defaults in domain-iot — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Hassan Sanchez· Sep 25, 2024

    Registry listing for domain-iot matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Piyush G· Sep 17, 2024

    domain-iot has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Oshnikdeep· Sep 13, 2024

    domain-iot is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Alexander Singh· Sep 1, 2024

    domain-iot reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Hassan Bhatia· Aug 20, 2024

    Registry listing for domain-iot matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Noor Agarwal· Aug 16, 2024

    domain-iot reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Shikha Mishra· Aug 8, 2024

    Solid pick for teams standardizing on skills: domain-iot is focused, and the summary matches what you get after install.

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