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概要と使いどころ
Use DGL-LifeSci and dgllife for molecular graph learning, molecule featurization, datasets, property prediction, reaction prediction, binding affinity, model-zoo/pretrained models, and molecular generation workflows.
日本語の概要は準備中です。原文の説明を表示しています。
Use DreamerV3 for world-model reinforcement learning training, embodied environment dataflow, JAX model internals, result operations, and troubleshooting.
日本語の概要は準備中です。原文の説明を表示しています。
Use DeepCTR-Torch for PyTorch CTR/recommender feature columns, single-task models, DIN/DIEN sequence models, and multi-task learning workflows.
日本語の概要は準備中です。原文の説明を表示しています。
Use DeepChem for molecular machine learning, data loading, featurization, model training, MoleculeNet, docking, and optional backend troubleshooting.
日本語の概要は準備中です。原文の説明を表示しています。
Routes Chainer workflows for training, export, distributed learning, ChainerX, and checkout maintenance.
日本語の概要は準備中です。原文の説明を表示しています。
Use CleanRL's single-file reinforcement learning scripts, evaluation utilities, experiment operations, and repository maintenance workflows safely.
日本語の概要は準備中です。原文の説明を表示しています。
Routes EEG, ECoG, MEG, and related electrophysiology deep-learning workflows through the braindecode Python package, including dataset construction, preprocessing, model training, augmentation, and interpretation.
日本語の概要は準備中です。原文の説明を表示しています。
Routes CogDL graph-learning workflows for experiments, graph data, models, training wrappers, and pipeline apps.
日本語の概要は準備中です。原文の説明を表示しています。
Operate the Deep Researcher Agent repository for autonomous deep-learning experiments, execution backends, provider/tool dispatch, GPU resources, durable progress, and research-support integrations.
日本語の概要は準備中です。原文の説明を表示しています。
Use AReaL for large-scale asynchronous LLM reinforcement learning, agentic RL, distributed post-training, inference/agent services, backend planning, and customization workflows.
日本語の概要は準備中です。原文の説明を表示しています。
Use ArcGIS API for Python for GIS administration, spatial dataframes, feature/raster analysis, mapping, location services, geospatial deep learning, app automation, and Knowledge Graph workflows.
日本語の概要は準備中です。原文の説明を表示しています。
Use ANTsPy/antspyx for medical image IO, ANTsImage metadata, image operations, registration, segmentation, visualization, and learning helper workflows.
日本語の概要は準備中です。原文の説明を表示しています。
Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.
日本語の概要は準備中です。原文の説明を表示しています。
Routes ACT++, ACT, Diffusion Policy, VINN, and MuJoCo simulation workflows for bimanual ALOHA episode data and imitation-learning tasks.
日本語の概要は準備中です。原文の説明を表示しています。
Guide agents through AgiBot X1 humanoid reinforcement-learning training, checkpoint playback, policy export, and MuJoCo sim2sim workflows with verified configuration contracts and explicit Isaac Gym limits.
日本語の概要は準備中です。原文の説明を表示しています。
Use AgileRL for reinforcement learning workflows: classical RL training, evolutionary HPO, evolvable networks, multi-agent PettingZoo training, offline/bandit data, and LLM fine-tuning.
日本語の概要は準備中です。原文の説明を表示しています。
Route AI-Optimizer reinforcement-learning collection tasks across model-based RL, easy-MARL, offline RL, safe command builders, and repository limitations.
日本語の概要は準備中です。原文の説明を表示しています。
Post-engagement knowledge harvest AND harness retrospective - after a box/bugbounty/pentest/CTF is completed, first diff how the engagement was EXECUTED against the skills/hooks that governed it (what discipline was skipped) and improve the harness, then sweep the whole engagement for GENERIC reusable knowledge NOT already in wiki/ and land it via the leak-gated stage->promote pipeline. Use at close-out or when asked to "extract learnings", "what did we learn", "harvest lessons into wiki", "distill this engagement", "post-mortem into the wiki", "what did we do wrong", "improve the harness from this box".
日本語の概要は準備中です。原文の説明を表示しています。
AI-powered E2E web testing — eyes and hands for AI coding tools. Declarative YAML scenarios, Playwright execution, visual matching (OpenCV + OCR), platform auto-detection (Flutter/React/Vue), learning DB. Install: npx skills add ksgisang/awt-skill --skill awt -g
日本語の概要は準備中です。原文の説明を表示しています。
AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.
日本語の概要は準備中です。原文の説明を表示しています。
Analyzes your recent Claude Code chat history to identify coding patterns, development gaps, and areas for improvement, curates relevant learning resources from HackerNews, and automatically sends a personalized growth report to your Slack DMs.
日本語の概要は準備中です。原文の説明を表示しています。
Learning designer quality check for Canvas LMS courses. Audits module structure, content completeness, publishing state, date consistency, and rubric coverage. Use when asked to "QC a course", "is this course ready", "pre-semester check", or "quality review".
日本語の概要は準備中です。原文の説明を表示しています。
Your personal DSA & LeetCode mentor. Use for problem explanations, progressive hints, code reviews, mock interviews, pattern recognition, complexity analysis, and custom problem generation. Automatically adapts to your learning style and request type.
日本語の概要は準備中です。原文の説明を表示しています。
Review whether a repository's coding-agent harness can reliably carry work from intent through controlled execution, verification, delivery, and learning. Use when asked to assess agent readiness, repeated agent failures, Rules/Skills/Hooks/Memory effectiveness, missing validation or recovery loops, or whether a harness repair improved later outcomes. Do not use for code-only audits, AGENTS-only audits, individual skill reliability reviews, or executing the task itself.
日本語の概要は準備中です。原文の説明を表示しています。