1,136 件 ・ 関連度順
概要と使いどころ
Use DreamerV2 2.2.0 for TensorFlow world-model reinforcement-learning training, custom Gym integration, typed experiment configuration, replay/checkpoint management, and JSONL/TensorBoard evaluation.
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 the DVC repository skill for data versioning, pipelines, experiments, remotes/cache, metrics/params/plots, Python API usage, and DVC codebase 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 DeepDanbooru for anime-style image tag estimation, project and Danbooru-style dataset preparation, TensorFlow training, image evaluation, TFLite conversion, and experimental Grad-CAM 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。
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。
Route ClearML Python SDK tasks across experiment tracking, data/versioned storage, remote execution CLIs, automation pipelines, HPO, schedulers, routers, and services.
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。
Use ARIS, the Auto Research in Sleep skill-based research harness, for setup, workflow routing, reviewer backend integration, state recovery, experiment operations, and repository 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。
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 Aim for experiment tracking SDK instrumentation, local/remote run storage, CLI/UI/server workflows, storage maintenance, and ML framework logging 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。
Research why neural architectures and training methods work through forward computation, geometry, gradients, optimization dynamics, historical experiments, and competing explanations. Use for deep mechanism questions about neural networks, AI models, layers, representations, or training; explain the meaning in plain language with evidence and explicit 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。
Methodology for auditing A/B testing of consent banners to ensure compliance with equal ease of acceptance and rejection. Covers CNIL enforcement patterns including the EUR 150M Google fine, dark pattern detection methodology, manipulative design identification, and regulatory-compliant experimentation boundaries.
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。
Product analytics and growth expert. Use when designing event tracking, defining metrics, running A/B tests, or analyzing retention. Covers AARRR framework, funnel analysis, cohort analysis, and experimentation.
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。
Design and optimize pricing strategies for SaaS products including tiering, packaging, value metrics, and experimentation. Use when setting initial pricing, optimizing conversion, expanding revenue, or when users ask about pricing strategy, monetization, or revenue optimization.
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。
Applies Judea Pearl's causal reasoning frameworks to distinguish correlation from causation, evaluate AI capabilities, and make counterfactual decisions. Reach for this skill whenever Claude encounters questions about causal inference, structural causal models, the limitations of deep learning, AGI, experimental design, covariate selection, or personalized decision-making. Trigger this skill for topics involving Bayesian networks, the do-calculus, the Ladder of Causation, or when a user tries to answer 'what if' or 'why' questions using purely observational data. Pearl's principles are essential for moving beyond probability calculus into true causal understanding.
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。
Apply the bioengineering, translational systems biology, and tissue modeling frameworks of Linda Griffith, Professor of Biological Engineering at MIT and tissue engineering pioneer. Use this skill whenever facing decisions, designs, or evaluations in bioengineering, tissue modeling, microphysiological systems (organs-on-chips), synthetic matrix design, disease subtyping, or drug target validation. Reach for this skill when evaluating preclinical models (human vs. animal), reframing overlooked or neglected pathologies into rigorous engineering challenges, selecting biomaterials, designing cell signal processing assays, or diagnosing operational flaws in experimental platforms (e.g., PDMS compound absorption or Matrigel lot variability).
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。
Applies the engineering and research philosophies of Jeff Dean, Chief Scientist at Google DeepMind and Google Research. Reach for this skill whenever you are designing large-scale distributed systems, optimizing latency and energy efficiency, or making architectural decisions about machine learning infrastructure. It should trigger automatically for topics involving hardware-ML co-design, model distillation, sparse activation, massively multi-task models, or scaling systems by 5x to 10x. Use this skill to evaluate system bottlenecks, transition from specialized to unified models, and optimize experimental velocity. Apply his mental models to avoid premature 100x scaling and to treat AI models as reasoning engines rather than memorization databases.
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。
Run the end-to-end viewer smoke test (`tests/viewer_smoke.sh`). Spawns `rezolus view` in upload-only, file, A/B, and proxy modes, exercises the API endpoints, and verifies experiment attach/detach. Use after any change touching `src/viewer/` or before opening a viewer-related PR.
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。
Throwaway experiments to validate an idea before build.
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。
ML paper pipeline: experiment design to submission.
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。
Turn vague or partially formed AI, computer-science, data-driven, and engineering-method research directions into evidence-grounded, falsifiable, paper-ready Research Idea Briefs through heuristic questioning, staged confirmation, literature positioning, novelty attack, and method-figure planning. Use when the user needs to clarify a research idea, formalize inputs/outputs/objectives/constraints, frame background and motivation, select 3-5 anchor papers, identify a defensible gap, derive 2-3 contributions, stress-test novelty, or design an input-to-output method framework and core-paper figure. Also trigger on requests such as 明确科研 idea、启发式梳理 idea、互动追问科研问题、梳理论文创新点、定义研究任务、找对标论文、把研究方向变成论文方案、画方法框架图. Do not use as the primary workflow for running experiments, exhaustive systematic reviews, drafting full manuscript sections, or polishing finished prose.
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 when the user wants an end-to-end AI4S research pipeline — broad direction or specific topic in, full research package out (exploration + literature survey + experiment + paper). Meta-skill that chains the four downstream skills in order. Pure markdown, no Python runtime.
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。
Deploy production recommendation systems with feature stores, caching, A/B testing. Use for personalization APIs, low latency serving, or encountering cache invalidation, experiment tracking, quality monitoring issues.
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。
Automate ML workflows with Airflow, Kubeflow, MLflow. Use for reproducible pipelines, retraining schedules, MLOps, or encountering task failures, dependency errors, experiment tracking issues.
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。
Systematic review response strategy guide for RebuttalStudio. Use when developing response strategies for reviewer comments, deciding how to classify concerns, or choosing between Accept/Defend/Clarify/Experiment approaches at any stage of the rebuttal pipeline.
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。