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Found 556 Skills
Guidance for implementing tensor parallelism in PyTorch, including ColumnParallelLinear and RowParallelLinear layers. This skill should be used when implementing distributed tensor parallel operations, sharding linear layers across multiple GPUs, or simulating collective operations like all-gather and all-reduce for parallel computation.
Use when researching or implementing anything related to Apple platforms (iOS, iPadOS, macOS, watchOS, tvOS, visionOS), Swift/Objective-C APIs, Apple frameworks, WWDC sessions, or Apple Developer Documentation. Triggers include: "find Apple's docs", "latest API guidance", "WWDC session", "platform availability", "SwiftUI/UIKit/AppKit/Combine/AVFoundation/etc.", or any Apple SDK coding question where authoritative docs are needed. Always use the apple-docs MCP tools for discovery and citations instead of general web search.
프로젝트의 모든 verify 스킬을 순차 실행하여 통합 검증 보고서를 생성합니다. 기능 구현 후, PR 전, 코드 리뷰 시 사용.
Guidance for implementing Adaptive Rejection Sampling (ARS) algorithms. This skill should be used when implementing rejection sampling methods, log-concave distribution samplers, or statistical sampling algorithms that require envelope construction and adaptive updates. It provides procedural approaches, performance considerations, and verification strategies specific to ARS implementations.
Guide for designing DNA insertion primers for site-directed mutagenesis (SDM) using Q5 or similar kits. This skill should be used when tasks involve inserting DNA sequences into plasmids, designing mutagenesis primers, or working with PCR-based insertion methods. Provides verification strategies, common pitfalls, and procedural guidance for correct primer design.
Guide for debugging and fixing bugs in the OCaml garbage collector, particularly memory management issues in the runtime's sweeping and allocation code. This skill applies when working on OCaml runtime C code, investigating segfaults in GC operations, or fixing pointer arithmetic bugs in memory managers with size-classed pools and run-length encoding.
Guidance for implementing proper asyncio task cancellation with signal handling in Python. This skill applies when implementing concurrent task runners that need graceful shutdown, handling KeyboardInterrupt/SIGINT in asyncio contexts, or managing task cleanup when using semaphores for concurrency limiting. Use when tasks involve asyncio.gather, CancelledError handling, or cleanup of tasks that haven't started execution.
Guidance for developing CoreWars warriors that achieve target win rates against specific opponents. This skill should be used when tasks involve writing, optimizing, or debugging Redcode assembly warriors for the CoreWars programming game, particularly when win rate thresholds must be met against multiple opponents.
Guidance for creating standalone CLI tools that perform neural network inference by extracting PyTorch model weights and reimplementing inference in C/C++. This skill applies when tasks involve converting PyTorch models to standalone executables, extracting model weights to portable formats (JSON), implementing neural network forward passes in C/C++, or creating CLI tools that load images and run inference without Python dependencies.
Guidance for building and fixing Cython extensions, particularly for numpy compatibility issues. This skill should be used when tasks involve compiling Cython code, fixing deprecated numpy type errors, or resolving compatibility issues between Cython extensions and modern numpy versions (2.0+).
Guidance for building Caffe from source and training CIFAR-10 models. This skill applies when tasks involve compiling Caffe deep learning framework, configuring Makefile.config, preparing CIFAR-10 dataset, or training CNN models with Caffe solvers. Use for legacy ML framework installation, LMDB dataset preparation, and CPU-only deep learning training tasks.
Expert assistance for OpenTUI with SolidJS. Use for reactive components, signals, fine-grained reactivity, JSX patterns, and SolidJS-specific optimization.