framework-internals
29.3K
multi-stage-dockerfile
Create optimized multi-stage Dockerfiles for any language or framework
github
development
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framework-internals
29.3K
refactor-method-complexity-reduce
Refactor given method `${input:methodName}` to reduce its cognitive complexity to `${input:complexityThreshold}` or below, by extracting helper methods.
github
development
framework-internals
29.3K
semantic-kernel
Create, update, refactor, explain, or review Semantic Kernel solutions using shared guidance plus language-specific references for .NET and Python.
github
development
framework-internals
25.6K
add-sgl-kernel
Step-by-step tutorial for adding a heavyweight AOT CUDA/C++ kernel to sgl-kernel (including tests & benchmarks)
sgl-project
development
framework-internals
23.8K
paddle-design-compiler
Use when working with Paddle 3.0 compiler full pipeline: SOT (Symbolic Opcode Translator) for bytecode-level dy2st graph capture, PIR (Paddle IR) for SSA-based intermediate representation, CINN for fused CUDA kernel generation, operator decomposition (Prim), or the end-to-end flow from Python eager code to optimized GPU execution.
PaddlePaddle
development
framework-internals
23.8K
paddle-phi-kernel
Use when working with Paddle's PHI kernel system: registering new kernels, debugging kernel selection/dispatch, understanding code auto-generation from YAML, or implementing operator decomposition via the combination mechanism.
PaddlePaddle
development
framework-internals
23.8K
paddle-op-dev
PaddlePaddle (飞桨) C++ 算子开发指南。提供从 YAML 配置、InferMeta 函数、Kernel 实现、Python API 封装、单元测试到编译验证的完整算子开发流程指导。在以下场景使用此 skill：(1) 为 Paddle 框架新增 C++ 算子 (2) 修改或调试已有 Paddle 算子 (3) 编写算子的 YAML 配置、InferMeta、Kernel、Python API 或单元测试 (4) 理解 Paddle 算子开发架构和流程 (5) 编译 Paddle 并验证算子正确性
PaddlePaddle
development
framework-internals
22.2K
token-efficiency
Activate ultra-compressed output mode for maximum token efficiency. Use when context is running low, user requests brevity, or dealing with large-scale operations.
SuperClaude-Org
development
framework-internals
18.8K
python-sdk
Python SDK patterns for Opik. Use when working in sdks/python, on SDK APIs, integrations, or message processing.
comet-ml
development
framework-internals
18.8K
typescript-sdk
TypeScript SDK patterns for Opik. Use when working in sdks/typescript.
comet-ml
development
framework-internals
18.1K
pennylane
Hardware-agnostic quantum ML framework with automatic differentiation. Use when training quantum circuits via gradients, building hybrid quantum-classical models, or needing device portability across IBM/Google/Rigetti/IonQ. Best for variational algorithms (VQE, QAOA), quantum neural networks, and integration with PyTorch/JAX/TensorFlow. For hardware-specific optimizations use qiskit (IBM) or cirq (Google); for open quantum systems use qutip.
K-Dense-AI
development
framework-internals
18.1K
pytorch-lightning
Deep learning framework (PyTorch Lightning). Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callbacks, logging (W&B, TensorBoard), distributed training (DDP, FSDP, DeepSpeed), for scalable neural network training.
K-Dense-AI
development
framework-internals
17.8K
add-new-jit-ee-api
Add a new API to the JIT-VM (aka JIT-EE) interface in the codebase.
dotnet
development
framework-internals
17.8K
extensions-review
Guidance for writing and modifying Microsoft.Extensions.* and System.IO.Compression code in dotnet/runtime. Covers DI lifetime management, configuration binding, options validation, logging provider patterns, caching semantics, compression format compliance, and host lifecycle. For full code review, delegates to the @extensions-reviewer agent. Trigger words: Microsoft.Extensions, IServiceCollection, IConfiguration, ILogger, IHost, IMemoryCache, IOptions, ZipArchive, HttpClientFactory, IFileProvider, IChangeToken.
dotnet
development
framework-internals
17.6K
tensorrt-llm
Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling.
davila7
development
framework-internals
17.6K
pytorch-fsdp
Expert guidance for Fully Sharded Data Parallel training with PyTorch FSDP - parameter sharding, mixed precision, CPU offloading, FSDP2
davila7
development
framework-internals
17.6K
pennylane
Cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Enables building and training quantum circuits with automatic differentiation, seamless integration with PyTorch/JAX/TensorFlow, and device-independent execution across simulators and quantum hardware (IBM, Amazon Braket, Google, Rigetti, IonQ, etc.). Use when working with quantum circuits, variational quantum algorithms (VQE, QAOA), quantum neural networks, hybrid quantum-classical models, molecular simulations, quantum chemistry calculations, or any quantum computing tasks requiring gradient-based optimization, hardware-agnostic programming, or quantum machine learning workflows.
davila7
development
framework-internals
17.6K
gguf-quantization
GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.
davila7
development
framework-internals
17.6K
optimizing-attention-flash
Optimizes transformer attention with Flash Attention for 2-4x speedup and 10-20x memory reduction. Use when training/running transformers with long sequences (>512 tokens), encountering GPU memory issues with attention, or need faster inference. Supports PyTorch native SDPA, flash-attn library, H100 FP8, and sliding window attention.
davila7
development
framework-internals
16.5K
golang-expert
Go programming expert for goroutines, channels, interfaces, modules, and concurrency patterns
RightNow-AI
development
framework-internals
15.3K
iii-rust-sdk
Rust SDK for the iii engine. Use when building high-performance workers, registering functions, or invoking triggers in Rust.
iii-hq
development