luckyegg168
Community@luckyegg168
luckyegg168 provides Rust development skills covering ownership, async runtimes, FFI interop, testing, performance, CI/CD, and Codex-based engineering utilities.
Agent Skills by luckyegg168
Showing 20 vetted skills indexed across 3 GitHub repositories.
rust-prompt-templates
Provides structured prompt templates for AI-assisted Rust code generation, debugging, and review.
rust-ffi-interop
Implement Rust FFI bindings for Python, C, and WASM using PyO3, cbindgen, and bindgen.
rust-crate-selection
Recommends Rust crates by domain and manages Cargo.toml versions and features.
rust-project-init
Initialize Rust projects with Cargo.toml templates, workspace configuration, and directory structure guidance.
rust-unsafe-guide
Guides writing, encapsulating, and verifying unsafe Rust code with Miri.
rust-error-handling
Implements Rust error handling with Result, Option, thiserror, and anyhow patterns.
rust-ownership-patterns
Diagnose and fix Rust ownership, borrowing, and lifetime compiler errors.
rust-performance
Profile and optimize Rust code using criterion benchmarks, flamegraph, and compiler tuning.
rust-ci-deploy
Configure Rust CI/CD pipelines with GitHub Actions, cross-compilation, and automated releases.
rust-async-runtime
Implements Rust asynchronous programming patterns using tokio runtime, channels, and task spawning.
rust-testing
Write and run Rust unit, integration, property, and async tests with proptest and mockall.
rust-design-patterns
Implements idiomatic Rust design patterns including Builder, Typestate, RAII, and Strategy.
omniweaving
Automate OmniWeaving deployment, model management, and six video inference tasks.
codex-engineering
Automate engineering workflows for slide generation, PR reviews, and API migration planning.
codex-evaluation
Automate eval-driven improvement loops for Codex tasks with deterministic scoring.
codex-use-cases
Consolidate OpenAI Codex use cases into a structured knowledge base with prompts and workflows.
codex-data
Automate data analysis workflows with pandas, statsmodels, and scikit-learn.
codex-frontend
Translate design references into responsive frontend code with Playwright visual tests.
codex-integrations
Integrate Codex with third-party platforms for cross-platform automation.
codex-mobile
Scaffold SwiftUI iOS/macOS projects and configure navigation via CLI.
Frequently Asked Questions About luckyegg168
FAQPage SchemaWhat tasks can I accomplish using luckyegg168's Rust skills?▼
You can initialize Cargo workspaces, resolve ownership and lifetime compiler errors, apply thiserror/anyhow error handling, write proptest and mockall tests, optimize with criterion and flamegraph, build tokio async services, create PyO3/cbindgen FFI bindings, and configure CI/CD with cross-compilation and cargo-dist releases.
Who are luckyegg168's skills designed for?▼
Rust developers at all levels, from beginners learning ownership and borrow checking to systems engineers handling unsafe code, FFI interop, async runtimes, and performance tuning. Teams standardizing crate selection, design patterns, and CI/CD pipelines also benefit, plus engineers using Codex for reviews and evaluations.
How do I install and run luckyegg168's skills?▼
Clone the desired skill folder from the public repositories and load it into a compatible skill-enabled coding environment. Each skill activates via its native frontmatter trigger keywords, such as 'Rust error handling', 'tokio', or 'cargo workspace', then provides structured guidance, templates, and configuration examples.
Are luckyegg168's skills free and open source?▼
The skills are published as public repositories under the luckyegg168 account, with 30 public repos available at no cost. No paid tier or commercial license is indicated in the manifest; users can browse and reuse the prompt templates, guides, and configuration patterns directly from the registry.
What prerequisites do luckyegg168's Rust skills require?▼
A working Rust toolchain with Cargo is the core prerequisite. Specific skills assume additional components: tokio 1.44 for async, PyO3 0.23 and Python for FFI, criterion 2.0 for benchmarking, GitHub Actions or GitLab CI for deployment skills, and Miri for unsafe code verification.