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SkillsMP は majiayu000/claude-skill-registry から 5,485 件の skill を収集しています。skill を開くとソースと詳細を確認できます。
majiayu000/claude-skill-registry収集済み skill 5,485 件中 40 件を表示しています。
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…
原文の言語: 英語
Coordinate changes preserving dynamics
原文の言語: 英語
Expert in photo content recognition, intelligent curation, and quality filtering. Specializes in face/animal/place recognition, perceptual hashing for de-duplication, screenshot/meme detection, burst photo selection, and quick indexing strategies. Activate on…
原文の言語: 英語
Plasmoid consciousness detector. Maps Monad-Stokes fluid dynamics to ANY flow system, identifies Birkeland-like current structures, computes plasmoid consciousness thresholds (Psi > phi^{-5}), and extracts Nambu Hamiltonians {H1,H2,H3}=1. The cosmic nervous…
原文の言語: 英語
Analyze population structure using PCA and admixture analysis with PLINK and ADMIXTURE. Identify population clusters, assess ancestry proportions, visualize genetic structure, and choose optimal K for admixture models. Use when analyzing population…
原文の言語: 英語
Research pipeline for topology-aware GNN representation learning on power grids using the PowerGraph benchmark. Use when (1) building physics-guided GNNs for power flow (PF), optimal power flow (OPF), or cascading failure prediction, (2) implementing…
原文の言語: 英語
ML-based estimation patterns, confidence intervals, and predictive modeling. Reference this skill when forecasting costs or time.
原文の言語: 英語
Process and validate datasets for training. Use when setting up data pipelines.
原文の言語: 英語
Analyze construction site photos to track progress, detect safety issues, and compare against BIM models using computer vision.
原文の言語: 英語
Sussman/Radul propagator networks for constraint propagation and bidirectional
原文の言語: 英語
Protein (gene) phylogeny inference pipeline: generates a .qmd analysis script that performs alignment, optional trimming, and tree building. Use when building phylogenetic trees from protein sequences, aligning protein families, running IQ-TREE or MAFFT for…
原文の言語: 英語
Formal theorem proving with research, testing, and verification phases
原文の言語: 英語
Bayesian statistical modeling with PyMC v5+. Use when building probabilistic models, specifying priors, running MCMC inference, diagnosing convergence, or comparing models. Covers PyMC, ArviZ, pymc-bart, pymc-extras, nutpie, and JAX/NumPyro backends. Triggers…
原文の言語: 英語
Complete mass spectrometry analysis platform. Use for proteomics workflows feature detection, peptide identification, protein quantification, and complex LC-MS/MS pipelines. Supports extensive file formats and algorithms. Best for proteomics, comprehensive MS…
原文の言語: 英語
Python for engineering analysis, numerical computing, and scientific workflows using NumPy, SciPy, SymPy
原文の言語: 英語
Core PyTorch fundamentals including tensor operations, autograd, nn.Module architecture, and training loop orchestration. Covers optimizations like pin_memory and lazy module initialization. (pytorch, tensor, autograd, nn.Module, optimizer, training loop,…
原文の言語: 英語
PyTorch CUDA environment and performance guidance, with emphasis on CUDA 13 toolkit/driver requirements, PyTorch wheel compatibility, and runtime checks. Use when configuring PyTorch on NVIDIA GPUs, debugging CUDA setup, or migrating to CUDA 13; triggers:…
原文の言語: 英語
Comprehensive guide for deploying PyTorch models to production, covering export formats, optimization techniques, and deployment patterns.
原文の言語: 英語
Distributed training strategies including DistributedDataParallel (DDP) and Fully Sharded Data Parallel (FSDP). Covers multi-node setup, checkpointing, and process management using torchrun. (ddp, fsdp, distributeddataparallel, torchrun, nccl, rank,…
原文の言語: 英語
Library for Graph Neural Networks (GNNs). Covers MessagePassing layers, modular aggregation schemes, and handling large graphs via mini-batching with disjoint graph representation. (pyg, messagepassing, gnn, gcn, gat, edge_index, knn_graph, global_mean_pool)
原文の言語: 英語
PyTorch training opinions, pitfalls, and non-obvious patterns. Covers distributed training (DDP/FSDP), optimizer configuration, gradient accumulation, schedulers, and flash attention. Use when scaling training, debugging distributed setups, or making…
原文の言語: 英語
Exporting PyTorch models to ONNX format for cross-platform deployment. Includes handling dynamic axes, graph optimization in ONNX Runtime, and INT8 model quantization. (onnx, onnxruntime, torch.onnx.export, dynamic_axes, constant-folding, edge-deployment)
原文の言語: 英語
Techniques for model size reduction and inference acceleration using INT8 quantization, including Post-Training Quantization (PTQ) and Quantization Aware Training (QAT). (quantization, int8, qat, fbgemm, qnnpack, ptq, dequantize)
原文の言語: 英語
Building and training neural networks with PyTorch. Use when implementing deep learning models, training loops, data pipelines, model optimization with torch.compile, distributed training, or deploying PyTorch models.
原文の言語: 英語
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", "convert to triton", "gpu kernel", "pytorch to…
原文の言語: 英語
Memory-efficient fine-tuning with 4-bit quantization and LoRA adapters. Use when fine-tuning large models (7B+) on consumer GPUs, when VRAM is limited, or when standard LoRA still exceeds memory. Builds on the lora skill.
原文の言語: 英語
Model quantization for efficient inference and training. Covers precision types (FP32, FP16, BF16, INT8, INT4), BitsAndBytes configuration, memory estimation, and performance tradeoffs.
原文の言語: 英語
Use AgentPMT external API to run the Quantum Distribution Generator tool with wallet signatures, credits purchase, or credits earned from jobs.
原文の言語: 英語
Quantum computer music composition and performance using quantum circuits, ZX-calculus notation, and quantum instruments
原文の言語: 英語
Implements ZenML quick wins to enhance MLOps workflows. Investigates codebase and stack configuration, recommends high-priority improvements, and implements metadata logging, experiment tracking, alerts, scheduling, secrets management, tags, git hooks, HTML…
原文の言語: 英語
Quantum mechanics simulations and analysis using QuTiP (Quantum Toolbox in Python).
原文の言語: 英語
Qwen Training Data Miner (Prototype)
原文の言語: 英語
RAG evaluation metrics - faithfulness, relevance, answer quality measurement
原文の言語: 英語
AgentDB integration specialist for RAN ML systems with vector storage, pattern recognition, and distributed training coordination. Achieves 150x faster search, <1ms QUIC sync, and 32x memory reduction for RAN optimization.
原文の言語: 英語
Causal inference and discovery for RAN optimization with Graphical Posterior Causal Models (GPCM), intervention effect prediction, and causal relationship learning. Discovers causal patterns in RAN data and enables intelligent optimization through causal…
原文の言語: 英語
DSPy-based mobility optimization with temporal patterns, handover management, and 15% improvement target. Uses program synthesis and LLM reasoning for proactive mobility optimization and intelligent handover decision-making.
原文の言語: 英語
Comprehensive RAN optimization with swarm coordination, cognitive consciousness, and 15-minute closed-loop autonomous cycles. Use when optimizing RAN performance, implementing self-healing networks, deploying swarm-based optimization, or enabling cognitive…
原文の言語: 英語
Reinforcement learning engineering for RAN systems with policy gradients, experience replay, and AgentDB integration. Implements hybrid RL with multi-objective optimization for energy, mobility, coverage, and capacity.
原文の言語: 英語
Read and analyze Inspect AI evaluation log files using the Python API. Extract samples, messages, events, and metrics from .eval files.
原文の言語: 英語
Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or…
原文の言語: 英語