How does it handle over-engineering?

It enforces YAGNI (You Ain't Gonna Need It) principles, discouraging premature abstractions and only allowing code changes that are directly requested or necessary.

Is this skill specific to Rust?

While it contains general quality principles, the implementation patterns and examples are specifically tailored for Rust development and systems programming.

What is the primary focus of this skill?

The primary focus is system correctness and data integrity, ensuring that code fails loudly rather than corrupting data in production environments.

What documentation style does this skill promote?

It mandates documenting the 'why' behind code rather than the 'what,' ensuring comments provide architectural context rather than repeating the code logic.

Rust Production Code Quality

Name: Rust Production Code Quality
Author: tursodatabase

bytursodatabase

•

17,110

Database Management

Enforces strict correctness rules and efficient Rust patterns for building high-integrity production systems.

About

This skill equips Claude with the rigorous standards required for developing mission-critical systems, specifically optimized for the Turso and libSQL ecosystem. It prioritizes absolute correctness over developer convenience, promoting 'crash-over-corrupt' architectures, exhaustive error handling, and memory-efficient Rust implementations. By enforcing specific commenting styles and discouraging over-engineering, it ensures that the resulting codebase remains maintainable, performant, and free from hidden failure modes common in database and systems programming.

Key Features

Enforces 'Crash > Corrupt' safety principles for data integrity
Promotes memory-efficient Rust patterns and exhaustive pattern matching
Eliminates over-engineering by preventing premature abstractions
Standardizes 'Why-based' documentation and comment styles
Mandates explicit error handling and assertion-driven development
17,110 GitHub stars

Use Cases

Auditing existing Rust code for system safety and performance bottlenecks
Developing high-performance database internals or storage engines
Standardizing code quality across large-scale systems programming projects

About

Key Features

Enforces 'Crash > Corrupt' safety principles for data integrity
Promotes memory-efficient Rust patterns and exhaustive pattern matching
Eliminates over-engineering by preventing premature abstractions
Standardizes 'Why-based' documentation and comment styles
Mandates explicit error handling and assertion-driven development
17,110 GitHub stars

Use Cases

Auditing existing Rust code for system safety and performance bottlenecks
Developing high-performance database internals or storage engines
Standardizing code quality across large-scale systems programming projects