2,307 件 ・ 関連度順
概要と使いどころ
Build comprehensive ML pipelines, experiment tracking, and model registries with MLflow, Kubeflow, and modern MLOps tools. Implements automated training, deployment, and monitoring across cloud platforms. Use PROACTIVELY for ML infrastructure, experiment management, or pipeline automation.
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。
Worked qupai-system (曲牌体) opera scripts, act by act. Guan Hanqing's three surviving zaju (窦娥冤／救风尘／金线池 — twelve acts, 126 tunes, one mode and one rhyme per act, sung/spoken ratios); Tang Xianzu's 牡丹亭 (55 scenes, ~420 tunes, 集唐 closing quatrains, the 圆驾 south-north composite suite); Kong Shangren's 桃花扇 (44 scenes plus four unnumbered, the 副末 frame line, upper/lower halves, a fan's seven transformations, his sixteen 凡例); 长生殿 (Wang Jilie's fifty-scene staging table); Wei Minglun's 巴山秀才 (1983 Sichuan 高腔, 43 tunes, nine 帮腔 passages). Every case states its edition and collation status, then structure, tunes, stage conventions and transferable devices. Method is in sw-chinese-opera-qupai; this skill holds the worked scripts. Use when you want a real example of how a 传奇 or 杂剧 is assembled, need actual scene counts and tune counts to imitate, are judging where a famous script breaks prosody, or need models for 副末开场, 集唐下场诗, 南北合套 or 帮腔 (曲牌体全本／牡丹亭／桃花扇／窦娥冤／救风尘／长生殿／巴山秀才／唱段表／结构表).
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。
Reviews React Native TV apps for focus/D-pad navigation, 10-foot UI layout, TV playback/DRM integration, low-memory TV performance, and TV accessibility. Use when building, debugging, or reviewing react-native-tvos, Expo TV, Amazon Vega/Kepler, or React Native web TV targets where the issue depends on remote input, TV focus, TV packaging, TV hardware, or TV playback constraints.
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 someone asks to level up their AIOS, close an audit gap, find what to automate next, or improve one workflow. Walks the 3Ms from choosing the constraint to shipping one useful artifact or verified repair.
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。
Build, deploy, evaluate, optimize, fine-tune, and manage Microsoft Foundry agents, models, and resources end to end. USE FOR: foundry, azd ai agent, azd provision/deploy, hosted agent scaffold/develop/run/deploy/troubleshoot, prompt agent create, create agent, update agent, add tool to agent, invoke agent, agent.yaml, agent insights, pull agent insights, evaluate agent, batch eval, continuous eval, continuous monitoring, agent CI/CD, optimize prompt, improve prompt, prompt optimizer, optimize agent instructions, Agent Optimizer scaffold, dataset curation from traces, deploy model, model fine-tuning (SFT/DPO/RFT), Foundry project, RBAC, role assignment, permissions, quota, capacity, region, deployment failure, AI Services, create Foundry resource, knowledge index, customize deployment, onboard, availability, training-data, grader, distillation, large file upload. DO NOT USE FOR: Azure Functions, App Service, general Azure deploy (use azure-deploy), general Azure prep (use azure-prepare).
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 a source-bound company and market dossier into testable ideal-customer-profile hypotheses, buying roles, triggers, constraints, disqualifiers, standing, counterevidence, and research gaps. Use when a URL, company description, monitor profile, or evidence manifest needs to become defensible ICP inputs for buyer research or an AI-visibility prompt panel.
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。
Generate natural, controlled prompt variants from a target-blind design brief and prompt architecture while preserving approved jobs, acts, journeys, constraints, roles, locales, proximity bands, and evidence language. Use after architecture design and before contamination or semantic QA.
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。
Recover source-bound buyer jobs, struggling moments, desired progress, forces, workarounds, information acts, journey states, criteria, constraints, roles, locales, and authentic language. Use on approved ICP hypotheses plus customer, search, review, forum, procurement, support, or public-market evidence before designing an AI-visibility prompt architecture.
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。
Build production Roblox interfaces with ScreenGui/PlayerGui lifecycle, responsive UDim2 layouts, safe insets, reusable editable Instances, cross-device input and selection, restrained motion, cleanup, and multi-viewport verification. Use for Roblox HUDs, inventories, shops, settings, ability bars, ScrollingFrames, AutomaticSize, GuiService, gamepad focus, touch UI, or UI that clips, resets on respawn, looks generic, or was generated as one giant LocalScript.
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。
Machine learning experimentation reference for model-experimentation conventions, experiment tracking and reproducibility, dataset and model abstractions, ML engagement fundamentals, and model-production readiness. Use when standing up ML experimentation infrastructure or assessing whether a trained model is ready for production.
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。
Create levelled demo decks and narrated MP4s for any repository topic, with HVE Core as the default in the hve-core repository. Use when training or demo material is needed for L100 through L400 audiences.
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 Thinking coaching foundation knowledge: coach identity and philosophy, quality and fidelity constraints, method sequencing, coaching state schema, and the canonical deck workflow
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。
Author and validate durable data and ML feasibility studies using the Feasibility Study Interchange Profile, constrained YAML authority, UUID URN identity, lifecycle lineage, and evidence traceability. Use when assessing whether available data and technical evidence support a proposed outcome.
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 asked 兴趣班怎么选, 孩子报几个兴趣班合适, 课外班规划, 双减后还能报什么班, 寒暑假怎么安排, 预付费培训退费, or plan a child's out-of-school activities in mainland China. Produces a weekly timetable that protects sleep, homework and free play, a choice framework (the child's interest, purpose, cost, travel), a term budget, a check of each class against the 双减 rules on academic training and prepaid fees, contract questions to ask a provider, and a review point to drop what is not working, with rules marked to confirm locally.
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。
Interview the user with sharp, one-at-a-time questions to turn a vague request into a tight, complete brief any other skill can run on. Use when a request is fuzzy, under-specified, or 'help me think this through', or before running a skill that needs inputs the user hasn't given. Produces a structured brief (goal, audience, constraints, success criteria) and hands off to the right skill — by interrogating, not guessing.
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。
Plan an apprentice's or new laborer's first week so they're useful by Friday and safe from hour one — day-by-day teaching order, the safety non-negotiables stated before anything else, what they're allowed to touch unsupervised, and the check-out conversation that decides week two. Use when a tradesperson says 'my apprentice starts Monday', 'how do I train the new guy', or 'the apprentice is useless and I don't have time to teach'. Produces a first-week plan, a can/can't-touch list, and the Friday review script.
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。
JLCPCB PCB fabrication and assembly — BOM/CPL generation, basic vs extended parts, assembly constraints, design rules, ordering workflow. Use with KiCad for JLCPCB manufacturing. Use this skill when the user mentions JLCPCB, wants to order PCBs or assembled boards, needs prototype bare PCBs and stencils, wants to know JLCPCB design rules and capabilities, or is asking about PCB manufacturing costs or turnaround times. For gerber/CPL export, stencil ordering, and BOM management, see the `bom` skill.
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 asked for first-principles or Occam's-razor review, or when high-risk decisions involve competing constraints, fallback growth, duplicate owners, or architecture direction risk. Ordinary bug fixes stay on the fast path.
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。
Explore goals, constraints, and alternative approaches before choosing a design. Use when the user requests ideation or design exploration.
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-first ideation that explores user intent, constraints, and approaches before any planning or implementation. Use for brainstorming, ideation, exploring concepts, and evaluating approaches.
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。
Multi-cloud orchestration for ML workloads with automatic cost optimization. Use when you need to run training or batch jobs across multiple clouds, leverage spot instances with auto-recovery, or optimize GPU costs across providers.
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。
Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.
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。
Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train <1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.
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 cognitive science frameworks for creative thinking to CS and AI research ideation. Use when seeking genuinely novel research directions by leveraging combinatorial creativity, analogical reasoning, constraint manipulation, and other empirically grounded creative strategies.
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。