rust-iterators
Leverage iterator chains for efficient, lazy data processing without intermediate allocations. Use when processing collections.
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Leverage iterator chains for efficient, lazy data processing without intermediate allocations. Use when processing collections.
Build mithril-core shared infrastructure. Use when implementing storage, compression, hashing, or common types for the Mithril ML toolkit.
Instrument code with tracing spans and structured logging. Use for observability and performance analysis.
Implement GPU scheduling with VRAM budgets, work queues, and model lifecycle management. Use when building ML pipeline orchestrators.
Minimize allocations and cloning using references, Cow, and smart pointers. Use when optimizing hot paths or memory-constrained code.
Build mithril-checkpoint compression for PyTorch models. Use when implementing byte grouping, compression pipeline, or checkpoint I/O.
Run security scans locally (Semgrep, Trivy, Gitleaks) to detect vulnerabilities, secrets, and code issues before pushing. Use when the user wants to check for security issues, scan dependencies, or validate cod...
Initialize Rust projects with proper structure, essential crates, and Cargo.toml configuration. Use when starting new Rust binaries or libraries.
Manage Git worktrees for parallel feature development. Use when the user wants to work on multiple branches simultaneously, create worktrees, switch between features, open terminals/editors for worktrees, or ma...
Write async tests with tokio::test and configure multi-threaded test runtimes. Use when testing async code.
Manage concurrent operations with channels, semaphores, locks, and streams. Use when coordinating parallel work or limiting resource usage.
Build native Rust ML models with Candle framework. Use when implementing vision transformers, LLMs, or audio models with GPU acceleration.
Build mithril-dedup for ML dataset deduplication. Use when implementing MinHash, LSH, clustering, or document I/O.
Implement native Rust ML inference with Candle framework. Use when building GPU-accelerated ML pipelines without Python dependencies.
Structure Rust code using modules, traits, builders, and newtypes. Use when designing APIs, abstracting behavior, or preventing type confusion.
Build mithril-cache for torch.compile caching. Use when implementing content-addressable storage, cache keys, eviction, or framework hooks.
Create mocks using mockall and trait-based abstractions. Use when unit testing code with external dependencies.
Port HuggingFace models to Candle with correct tensor naming. Use when debugging weight loading errors or matmul issues.
Create new Claude Code skills with proper structure and conventions. Use when the user wants to create a new skill, scaffold skill templates, or understand skill anatomy.
Apply rustfmt and clippy linting standards for consistent, safe Rust code. Use before commits and in CI pipelines.
Expose Prometheus metrics with counters, gauges, and histograms. Use for production monitoring and alerting.
Implement the repository pattern for database abstraction with sqlx, transactions, and retry logic. Use when building data access layers.
Core Tokio patterns including runtime setup, spawn_blocking for CPU work, and avoiding blocking operations. Use for any async Rust development.
Design error types using thiserror for libraries and anyhow for applications. Use when defining error hierarchies or handling fallible operations.
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