Browse published AI skills maintained by EmilLindfors. Compare skill descriptions and follow each source repository for setup instructions.
Indexed skills
20Repository stars across indexed skills
45Repository forks
19Average repository stars per indexed skill
2Repository stars may repeat across skills from the same repository.
Enterprise-grade browser automation using WebDriver protocol. Use when the user needs to automate web browsers, perform web scraping, test web applications, fill forms, take screenshots, monitor performance, or...
Reviews test coverage and suggests missing test cases for error paths, edge cases, and business logic. Activates when users write tests or implement new features.
Reviews code architecture for hexagonal patterns, checks dependency directions, and suggests improvements for ports and adapters separation. Activates when users work with services, repositories, or architectur...
Reviews SQL queries and DataFrame operations for optimization opportunities including predicate pushdown, partition pruning, column projection, and join ordering. Activates when users write DataFusion queries o...
Provides guidance on reducing Lambda cold start times through binary optimization, lazy initialization, and deployment strategies. Activates when users discuss cold starts or deployment configuration.
Identifies deeply nested if-let expressions and suggests let chains for cleaner control flow. Activates when users write nested conditionals with pattern matching.
Provides guidance on creating custom error types with thiserror, including proper derive macros, error messages, and source error chaining. Activates when users define error enums or work with thiserror.
Guides users in creating rich domain models with behavior, value objects, and domain logic. Activates when users define domain entities, business rules, or validation logic.
Provides architectural guidance for data lake design including partitioning strategies, storage layout, schema design, and lakehouse patterns. Activates when users discuss data lake architecture, partitioning,...
Guides users on modern async patterns including native async fn in traits, async closures, and avoiding async-trait when possible. Activates when users work with async code.
Guides users on choosing between async and sync patterns for Lambda functions, including when to use tokio, rayon, and spawn_blocking. Activates when users write Lambda handlers with mixed workloads.
Helps design port traits and adapter implementations for external dependencies. Activates when users need to abstract away databases, APIs, or other external systems.
Guides users on error conversion patterns, From trait implementations, and the ? operator. Activates when users need to convert between error types or handle multiple error types in a function.
Guides users through migrating to Rust 2024 edition features including let chains, async closures, and improved match ergonomics. Activates when users work with Rust 2024 features or nested control flow.
Proactively analyzes Parquet file operations and suggests optimization improvements for compression, encoding, row group sizing, and statistics. Activates when users are reading or writing Parquet files or disc...
Introduces property-based testing with proptest, helping users find edge cases automatically by testing invariants and properties. Activates when users test algorithms or data structures.
Guides users on creating mock implementations for testing with traits, providing test doubles, and avoiding tight coupling to test infrastructure. Activates when users need to test code with external dependenci...
Proactively reviews error handling patterns and suggests improvements using Result types, proper error propagation, and idiomatic patterns. Activates when users write error handling code or use unwrap/expect.
Reviews AWS Lambda functions for performance, memory configuration, and cost optimization. Activates when users write Lambda handlers or discuss Lambda performance.
Ensures proper cloud storage operations with retry logic, error handling, streaming, and efficient I/O patterns. Activates when users work with object_store for S3, Azure, or GCS operations.
The default order uses GitHub repository stars from our indexed data. Stars belong to the source repository and may be shared by multiple skills. They indicate repository interest, not skill installs or an independent quality score.