add-rule
Add a new rule to the learned rules file
Découvrez les skills tools framework internals, comparez leurs dépôts et consultez leurs instructions.
Add a new rule to the learned rules file
O(n) instead of O(n log n)
avoid redundant computation
Generate CANN Ascend C operator code from Python reference
Optimize layers and use minimal base images.
Translate PyTorch implementations to Triton GPU kernels incrementally. Use when converting PyTorch code to Triton, optimizing GPU kernels, auditing/reviewing existing Triton code, or when user says "triton", "c...
Emit C output and a C header for embedding X07 in C/C++ via x07c --emit-c-header.
O(n) to O(1)
1M ops to 2K ops
Rust serialization/deserialization framework for JSON, YAML, TOML, and more.
enables hardware acceleration
Optimization standards for rebuilds and memory.
Expert performance analysis and optimization. Use when analyzing performance issues, optimizing slow code, reducing bundle size, or improving application speed.
Optimize mode for refactoring, performance improvement, and code cleanup. Use when user requests optimization or identifies slow code. Measures baseline, identifies bottlenecks, improves without changing behavi...
Optimize Sunset Pipeline Moonshot Integration for 32GB RAM + RTX 3060 GPU environment with practical scaling, resource-efficient implementations, and achievable milestones. Use when implementing AI development...
This document serves as some special instructions when working with Convex.
Implements visual shaders and shader code for 2D/3D effects including dissolve, outline, water, post-processing, and custom materials. Use when creating custom visual effects.
Implements optimization techniques for rendering, scripting, memory, and physics performance. Use when improving game performance for release.
Ottimizzazione Semantica e Fisica del Pensiero (SPAr).
Implements optimization techniques for rendering, scripting, memory, physics, and networking. Use when improving game performance, reducing lag, or preparing for mobile/low-end devices.
Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.