sqlite-extension-generator
Generate optimized SQLite extensions in C, Rust, or Mojo
Generate optimized SQLite extensions in C, Rust, or Mojo
Use flynt to convert old Python string formatting to f-strings. Activate when: (1) Converting %-formatting to f-strings, (2) Converting .format() calls to f-strings, (3) Modernizing string concatenation to f-strings, (4) Improving code readability through f-string adoption, or (5) Batch-converting legacy Python codebases.
Add tensor shape validation and documentation to PyTorch code. This skill should be used when working with PyTorch models to ensure tensor shapes are correct and well-documented with inline comments.
Optimize and implement GPU kernels using CubeCL for memory-efficient LLM training
Use ComputationalPaths path tactics to automate common RwEq goals (path_simp/path_auto/path_normalize), and structure calc-based proofs cleanly.
Memory layer operations for persistent session storage
Otimizador automรกtico de cรณdigo usando anรกlise evolutiva GEPA para melhorar qualidade, performance e manutenibilidade
Write idiomatic Python code with advanced features like decorators, generators, and async/await. Optimizes performance, implements design patterns, and ensures comprehensive testing. Use for ML training, analytics tools, performance profiling, or any Python heavy lifting.
Optimize PyTorch with torch.compile (TorchDynamo/Inductor), focusing on compile overhead, graph breaks, and benchmark methodology. Use when speeding up PyTorch models or debugging compile behavior; triggers: torch.compile, torchdynamo, inductor, graph break, pytorch optimization.
Convert Python loops to vectorized PyTorch tensor operations for performance. This skill should be used when optimizing computational bottlenecks in PyTorch code during Phase 4 performance optimization.
Fundamental NumPy operations including ndarray creation, dtypes, shape manipulation, and basic operations with a focus on memory alignment and data views. Triggers: numpy, ndarray, dtype, reshape, memory alignment, array-creation.
A template for skills that include executable code for deterministic operations.
Fixes type mismatch errors by adding appropriate casts or conversions.
Create FFI bindings between Lean 4 and C code. Use when working with foreign functions, native libraries, Metal, or system APIs.
Advanced 4-bit quantization techniques using Unsloth and BitsAndBytes for extreme VRAM efficiency (triggers: QLoRA, 4-bit, load_in_4bit, bnb-4bit, VRAM optimization, dynamic quantization).
Analyze the competitive and adjacent solution landscape to surface differentiation opportunities.
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GPU-safe parallel processing patterns for KINTSUGI to prevent OOM crashes and ensure Jupyter-compatible progress output
Exploiting custom interpreters and virtual machines
Step-by-step guide for adding new feature modules to the renderer process using feature-based architecture
Deep dive into memory layout, including strides, C vs Fortran order, and zero-copy view generation via stride tricks. Triggers: strides, C-order, Fortran-order, memory locality, stride_tricks.
Guides adding new Higher Inductive Types to the ComputationalPaths library. Use when creating new HITs, defining fundamental group (pi1) calculations, implementing encode-decode proofs, or adding new topological spaces.
Experto en optimizaciรณn de bajo nivel. Su misiรณn es garantizar 60 FPS constantes, gestiรณn de memoria eficiente mediante Isolates y disponibilidad offline total.