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Skills IA testing security framework internals

Découvrez les skills testing security framework internals, comparez leurs dépôts et consultez leurs instructions.

Catégories: testing security framework internals

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GitHub

implementing-python

qte77

Implements concise, streamlined Python code matching exact architect specifications. Use when writing Python code, creating modules, or when the user asks to implement features in Python.

GitHub

performance

dtbuchholz

Optimize code for speed and efficiency. Use this skill when profiling, identifying bottlenecks, or improving performance. Covers profiling tools, common patterns, and optimization strategies.

GitHub

pytorch-to-triton

biobenkj

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...

GitHub

x07-ffi-c

x07lang

Emit C output and a C header for embedding X07 in C/C++ via x07c --emit-c-header.

GitHub

performance-optimizer

larsnyg

Expert performance analysis and optimization. Use when analyzing performance issues, optimizing slow code, reducing bundle size, or improving application speed.

GitHub

mode-optimize

mahamaneharouna9-cpu

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...

GitHub

sunset-pipeline-rtx3060-optimized

zapabob

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...

GitHub

convex

emelleby

This document serves as some special instructions when working with Convex.

GitHub

shaders

taozhuo

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.

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.

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