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Found 103 Skills
Use when building C++ applications requiring modern C++20/23 features, template metaprogramming, or high-performance systems. Invoke for concepts, ranges, coroutines, SIMD optimization, memory management.
C++ coding standards based on the C++ Core Guidelines (isocpp.github.io). Use when writing, reviewing, or refactoring C++ code to enforce modern, safe, and idiomatic practices.
Use only when writing/updating/fixing C++ tests, configuring GoogleTest/CTest, diagnosing failing or flaky tests, or adding coverage/sanitizers.
Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.
Guidelines for modern C++ development with C++17/20 standards, memory safety, and performance optimization
Principal backend engineering intelligence for C++ systems and performance-critical services. Actions: plan, design, build, implement, review, fix, optimize, refactor, debug, secure, scale backend code and architectures. Focus: correctness, memory safety, latency, reliability, observability, scalability, operability.
Expert coding guide for OpenHarmony C++ development. Use this skill when writing, refactoring, or reviewing C++ code for OpenHarmony projects. It enforces strict project-specific conventions (naming, formatting, headers) and critical security requirements (input validation, memory safety).
Write modern C++ with RAII, smart pointers, and STL. Use for C++ development, memory safety, or performance optimization.
Automatically sorts C/C++ header files (#include statements) with full support for conditional compilation blocks. Use when Claude needs to organize
C++ Reinforcement Learning best practices using libtorch (PyTorch C++ frontend) and modern C++17/20. Use when: - Implementing RL algorithms in C++ for performance-critical applications - Building production RL systems with libtorch - Creating replay buffers and experience storage - Optimizing RL training with GPU acceleration - Deploying RL models with ONNX Runtime
C and C++ compiler toolchain skill covering GCC, Clang/LLVM, build modes, warnings, sanitizers, static analysis, LTO, PGO, C++20/23/26 features, and debugging. Use when writing or reviewing C/C++ code, choosing compiler flags, interpreting errors or warnings, enabling sanitizers, running clang-tidy or cppcheck, optimizing builds, working with C++20 modules or C23 features, or troubleshooting linker issues.
C++ template skill for reading template errors and optimizing compile times. Use when deciphering template error stacks, setting -ftemplate-backtrace-limit, writing concepts and requires-clauses, understanding SFINAE vs concepts, or profiling template instantiation bottlenecks with Templight. Activates on queries about C++ templates, template error messages, concepts, requires expressions, SFINAE, template metaprogramming, or slow template compilation.