296건 · 관련도순
Quantum physics simulation library for open quantum systems. Use when studying master equations, Lindblad dynamics, decoherence, quantum optics, or cavity QED. Best for physics research, open system dynamics, and educational simulations. NOT for circuit-based quantum computing—use qiskit, cirq, or pennylane for quantum algorithms and hardware execution.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Provides guidance for mechanistic interpretability research using TransformerLens to inspect and manipulate transformer internals via HookPoints and activation caching. Use when reverse-engineering model algorithms, studying attention patterns, or performing activation patching experiments.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Production-ready reinforcement learning algorithms (PPO, SAC, DQN, TD3, DDPG, A2C) with scikit-learn-like API. Use for standard RL experiments, quick prototyping, and well-documented algorithm implementations. Best for single-agent RL with Gymnasium environments. For high-performance parallel training, multi-agent systems, or custom vectorized environments, use pufferlib instead.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Fast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in <20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alignments, handle padding/truncation. Integrates seamlessly with transformers. Use when you need high-performance tokenization or custom tokenizer training.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Language-independent tokenizer treating text as raw Unicode. Supports BPE and Unigram algorithms. Fast (50k sentences/sec), lightweight (6MB memory), deterministic vocabulary. Used by T5, ALBERT, XLNet, mBART. Train on raw text without pre-tokenization. Use when you need multilingual support, CJK languages, or reproducible tokenization.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Cheminformatics toolkit for fine-grained molecular control. SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints, substructure search, 2D/3D generation, similarity, reactions. For standard workflows with simpler interface, use datamol (wrapper around RDKit). Use rdkit for advanced control, custom sanitization, specialized algorithms.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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 data processing. For simple spectral comparison and metabolite ID use matchms.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Algorithmic philosophies are computational aesthetic movements that are then expressed through code. Output .md files (philosophy), .html files (interactive viewer), and .js files (generative algorithms).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Fast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in <20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alignments, handle padding/truncation. Integrates seamlessly with transformers. Use when you need high-performance tokenization or custom tokenizer training.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Algorithmic philosophies are computational aesthetic movements that are then expressed through code. Output .md files (philosophy), .html files (interactive viewer), and .js files (generative algorithms).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Apply practical DAG decomposition, transitive-edge reduction, and reachability indexing to dense dependency graphs. Use when low width and repeated queries justify preprocessing. NOT for cyclic graphs, one-off graph checks, or exact-minimum-chain requirements.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Apply practical DAG decomposition, transitive-edge reduction, and reachability indexing to dense dependency graphs. Use when low width and repeated queries justify preprocessing. NOT for cyclic graphs, one-off graph checks, or exact-minimum-chain requirements.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build and validate a static DAG reachability index from source-qualified path/chain decompositions and partial transitive-edge filtering. NOT for treating a partial edge filter as a complete transitive reduction or runtime scheduler.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Validate finite-DAG order objectives and construct reachability-chain or direct-edge path partitions with explicit proof boundaries. NOT for equating reachability chains with direct-edge paths without checking the chosen objective.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Analyze undirected cycle structure within a valid DAG using the source paper's orientation and contraction method. Use for cycle-basis analysis with explicit graph and reduction choices. NOT for directed feedback loops, workflow deadlocks, or runtime resilience claims.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Model an organization with BDI-inspired perceptions, expressed goals, accommodations, and levels of agency. Use for socio-technical diagnosis where actors interpret the same situation differently and agreement is partial. NOT for individual AgentSpeak execution, normative conflict algorithms, or ordinary workflow diagrams.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
API gateway and reverse proxy configuration with Kong, Nginx, Traefik, routing, and auth middleware. Activate on: API gateway, reverse proxy, Kong, Nginx, Traefik, load balancer, ingress, auth middleware. NOT for: rate limiting algorithms (use api-rate-limiting-throttling-expert), service mesh (use service-mesh-microservices-expert).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Generates Rate Limiter pattern for PHP 8.5. Creates request throttling with token bucket, sliding window, and fixed window algorithms. Includes unit tests.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Algorithmic philosophies are computational aesthetic movements that are then expressed through code. Output .md files (philosophy), .html files (interactive viewer), and .js files (generative algorithms).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.