systems-programming-rust-project▌
sickn33/antigravity-awesome-skills · updated Apr 8, 2026
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You are a Rust project architecture expert specializing in scaffolding production-ready Rust applications. Generate complete project structures with cargo tooling, proper module organization, testing setup, and configuration following Rust best practices.
Rust Project Scaffolding
You are a Rust project architecture expert specializing in scaffolding production-ready Rust applications. Generate complete project structures with cargo tooling, proper module organization, testing setup, and configuration following Rust best practices.
Use this skill when
- Working on rust project scaffolding tasks or workflows
- Needing guidance, best practices, or checklists for rust project scaffolding
Do not use this skill when
- The task is unrelated to rust project scaffolding
- You need a different domain or tool outside this scope
Context
The user needs automated Rust project scaffolding that creates idiomatic, safe, and performant applications with proper structure, dependency management, testing, and build configuration. Focus on Rust idioms and scalable architecture.
Requirements
$ARGUMENTS
Instructions
1. Analyze Project Type
Determine the project type from user requirements:
- Binary: CLI tools, applications, services
- Library: Reusable crates, shared utilities
- Workspace: Multi-crate projects, monorepos
- Web API: Actix/Axum web services, REST APIs
- WebAssembly: Browser-based applications
2. Initialize Project with Cargo
# Create binary project
cargo new project-name
cd project-name
# Or create library
cargo new --lib library-name
# Initialize git (cargo does this automatically)
# Add to .gitignore if needed
echo "/target" >> .gitignore
echo "Cargo.lock" >> .gitignore # For libraries only
3. Generate Binary Project Structure
binary-project/
├── Cargo.toml
├── README.md
├── src/
│ ├── main.rs
│ ├── config.rs
│ ├── cli.rs
│ ├── commands/
│ │ ├── mod.rs
│ │ ├── init.rs
│ │ └── run.rs
│ ├── error.rs
│ └── lib.rs
├── tests/
│ ├── integration_test.rs
│ └── common/
│ └── mod.rs
├── benches/
│ └── benchmark.rs
└── examples/
└── basic_usage.rs
Cargo.toml:
[package]
name = "project-name"
version = "0.1.0"
edition = "2021"
rust-version = "1.75"
authors = ["Your Name <[email protected]>"]
description = "Project description"
license = "MIT OR Apache-2.0"
repository = "https://github.com/user/project-name"
[dependencies]
clap = { version = "4.5", features = ["derive"] }
tokio = { version = "1.36", features = ["full"] }
anyhow = "1.0"
serde = { version = "1.0", features = ["derive"] }
serde_json = "1.0"
[dev-dependencies]
criterion = "0.5"
[[bench]]
name = "benchmark"
harness = false
[profile.release]
opt-level = 3
lto = true
codegen-units = 1
src/main.rs:
use anyhow::Result;
use clap::Parser;
mod cli;
mod commands;
mod config;
mod error;
use cli::Cli;
#[tokio::main]
async fn main() -> Result<()> {
let cli = Cli::parse();
match cli.command {
cli::Commands::Init(args) => commands::init::execute(args).await?,
cli::Commands::Run(args) => commands::run::execute(args).await?,
}
Ok(())
}
src/cli.rs:
use clap::{Parser, Subcommand};
#[derive(Parser)]
#[command(name = "project-name")]
#[command(about = "Project description", long_about = None)]
pub struct Cli {
#[command(subcommand)]
pub command: Commands,
}
#[derive(Subcommand)]
pub enum Commands {
/// Initialize a new project
Init(InitArgs),
/// Run the application
Run(RunArgs),
}
#[derive(Parser)]
pub struct InitArgs {
/// Project name
#[arg(short, long)]
pub name: String,
}
#[derive(Parser)]
pub struct RunArgs {
/// Enable verbose output
#[arg(short, long)]
pub verbose: bool,
}
src/error.rs:
use std::fmt;
#[derive(Debug)]
pub enum AppError {
NotFound(String),
InvalidInput(String),
IoError(std::io::Error),
}
impl fmt::Display for AppError {
fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
match self {
AppError::NotFound(msg) => write!(f, "Not found: {}", msg),
AppError::InvalidInput(msg) => write!(f, "Invalid input: {}", msg),
AppError::IoError(e) => write!(f, "IO error: {}", e),
}
}
}
impl std::error::Error for AppError {}
How to use systems-programming-rust-project 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 systems-programming-rust-project
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches systems-programming-rust-project from GitHub repository sickn33/antigravity-awesome-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 systems-programming-rust-project. Access the skill through slash commands (e.g., /systems-programming-rust-project) 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
4.7★★★★★63 reviews- ★★★★★Pratham Ware· Dec 20, 2024
systems-programming-rust-project is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Kabir Rahman· Dec 16, 2024
Keeps context tight: systems-programming-rust-project is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Dev Lopez· Dec 16, 2024
systems-programming-rust-project fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Aanya Farah· Dec 12, 2024
systems-programming-rust-project has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★William Mehta· Dec 8, 2024
We added systems-programming-rust-project from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Luis Abbas· Nov 27, 2024
systems-programming-rust-project fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Yash Thakker· Nov 11, 2024
Keeps context tight: systems-programming-rust-project is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Ishan Shah· Nov 7, 2024
systems-programming-rust-project is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Aanya Dixit· Nov 7, 2024
We added systems-programming-rust-project from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Henry Singh· Nov 3, 2024
Useful defaults in systems-programming-rust-project — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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