Claude Code skill that architects production-ready Rust applications with complete documentation, ADRs, guardrails, and handoff materials for AI collaboration.
git clone https://github.com/nanlong/rust-architect.gitRust Architect is a Claude Code skill that generates comprehensive project documentation packages for building complex Rust systems following industry best practices. It creates complete directory structures, 20+ documentation files including architecture guides, Architecture Decision Records, and AI collaboration guardrails designed for Director/Implementor workflows. The skill automatically researches domain-specific patterns for web services, CLI tools, and backend systems using tokio, axum, and sqlx, then generates handoff materials that enable AI agents to successfully implement complex Rust applications with proven architectural patterns.
Install via Claude Code plugin with `/plugin install github.com/nanlong/rust-architect` or invoke with `/skills rust-architect`. The skill will ask about your project domain, tech stack, location, structure style, and special requirements, then launch expert Task agents to research patterns and generate your documentation package.
Architecting REST API services with axum and sqlx
Creating multi-crate workspace projects with clear separation of concerns
Establishing AI collaboration guardrails for Director/Implementor workflows
Generating Architecture Decision Records for async/await patterns and error handling
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/nanlong/rust-architectCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Act as a Rust Architect. Design a robust, scalable system for [COMPANY] in the [INDUSTRY] sector. The system should handle [DATA] with the following requirements: [REQUIREMENTS]. Provide a detailed architecture diagram, key components, and technology stack recommendations.
# Rust System Architecture for GreenTech Solutions
## Overview
A scalable IoT data processing system for GreenTech Solutions, handling sensor data from 10,000+ devices with real-time analytics and predictive maintenance capabilities.
## Architecture Diagram
```mermaid
flowchart TD
A[IoT Devices] --> B[Edge Nodes]
B --> C[Data Ingestion Layer]
C --> D[Processing Cluster]
D --> E[Storage Layer]
D --> F[Analytics Engine]
E --> G[API Gateway]
F --> G
G --> H[Dashboard]
```
## Key Components
- **Edge Nodes**: Rust-based lightweight processors for initial data filtering
- **Data Ingestion Layer**: Kafka cluster for high-throughput message handling
- **Processing Cluster**: Kubernetes-managed Rust services for parallel data processing
- **Storage Layer**: Time-series database (InfluxDB) with Rust client libraries
- **Analytics Engine**: Machine learning models integrated with Rust's ndarray and linfa crates
## Technology Stack
- **Programming Language**: Rust (with tokio for async processing)
- **Message Broker**: Apache Kafka
- **Containerization**: Docker
- **Orchestration**: Kubernetes
- **Database**: InfluxDB
- **Monitoring**: Prometheus with Rust exporters
## Performance Considerations
- Designed to handle 100,000+ messages per second
- Sub-millisecond latency for critical path operations
- Automatic scaling based on load metrics
## Security
- End-to-end encryption using Rust's ring crate
- Role-based access control implemented at API gateway
- Regular security audits using Rust's built-in toolsAI assistant built for thoughtful, nuanced conversation
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