「learning」の検索結果

Run a complete design workflow -- from establishing context through four progressive design levels (Capabilities, Components, Interactions, Contracts) to an approved blueprint. Composes knowledge-priming, context-anchoring, learning-harvest, collaborative-judgment, design-first, architecture, and domain-driven-design into one process. Handles both new features (create context doc) and resuming existing work (load context doc). Level 5 (Implementation) is delegated to code-forge. Use when starting a design, planning architecture, or when the user says 'design a feature', 'blueprint', 'start designing', 'plan the architecture', or 'let's design before coding'.

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。

DyNote: systematically and efficiently extract raw Douyin/DY video data and analyze videos, comments, accounts, hashtags, and short-video scenes into evidence-graded learning notes, summaries, research briefs, scripts, and knowledge-base material. Use when the user asks to 抓取/提取/整理 抖音视频字幕、视频文案、ASR 转写、Qwen3-ASR 中文转写、原始材料归档、学习笔记、analysis plan、note budget、避免返工、复用已有素材、评论洞察、账号分析、赛道/话题研究、竞品拆解、电商/本地生活视频分析、事实核查、自动搜索素材, save Douyin content as Markdown/TXT, or use subtitle/local ASR as the factual spine with logged-in Douyin Web built-in AI / Doubao fallback as visual or quick-reading supplements.

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 Xiaohongshu (XHS / RedNote) cover images using GPT Image 2 / Codex image generation first, with the legacy Gemini CLI as fallback. Triggers for cover generation: "生成封面", "小红书封面", "xhs封面", "制作封面", "帮我做张图", "XHS cover", "RedNote cover". Triggers for cover editing: "修改封面", "改一下封面", "上一张图基础上", "基于这张图调整", "不要重新做", "edit this cover". Triggers for style learning: "学习这个风格", "提取风格", "我想自定义风格", "这张图的风格", "learn this style", "extract style". Always trigger this skill when user mentions 小红书/XHS/RedNote together with 封面/cover/图片, OR when user uploads a reference image and asks about creating a similar style.

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。

Turns folders, codebases, websites, PDFs, documents, and research topics into source-grounded HTML documents, deterministic narrated videos, or complete learning courses; also publishes and revises collaborative HTML pages through the HTML Docs CLI, REST API, and MCP server.

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 an instructor asks for a MATLAB AI tutor setup guide, adoption guide, pilot plan, course-specific rollout, or recommended tutor configuration based on a learning objective, course title, course description, module description, or lab description.

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 reviewing, auditing, scoring, or improving a real or synthetic MATLAB AI tutor transcript, tutoring prompt, generated lesson, exercise, feedback sequence, or skill behavior for MATLAB accuracy, active learning, assignment guardrails, feedback quality, debugging support, transfer prompts, and instructor-facing quality recommendations.

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 an instructor asks for a MATLAB Grader assessment setup guide, adoption guide, pilot plan, course-specific rollout, QTI 3 sharing workflow, or recommended MATLAB Grader assessment-item-generation configuration based on a learning objective, course title, course description, module description, lab description, or assessment goal.

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 MATLAB Course Designer MATLAB Exercise learning activities by wrapping the existing matlab-generate-grader-assessments skill, then validating generated solution.m, template.m, and tests.m files with MATLAB MCP Server tool calls. Use when the user asks to create a MATLAB Exercise, MATLAB course activity, Course Designer MATLAB activity, validated MATLAB solution file, or MATLAB Exercise component folder.

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。

Capture durable, project-specific learnings from the work just done into the knowledge/ base — gotchas, implementation quirks, things looked up or web-searched, live-API behaviours, and design decisions (as ADRs). Use before opening a PR (it runs automatically in /deliver), or any time you've learned something worth remembering. Records only non-obvious, reusable facts; skips anything already in the repo, CLAUDE.md, or git history.

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。

DyNote: systematically and efficiently extract raw Douyin/DY video data and analyze videos, comments, accounts, hashtags, and short-video scenes into evidence-graded learning notes, summaries, research briefs, scripts, and knowledge-base material. Use when the user asks to 抓取/提取/整理 抖音视频字幕、视频文案、ASR 转写、Qwen3-ASR 中文转写、原始材料归档、学习笔记、analysis plan、note budget、避免返工、复用已有素材、评论洞察、账号分析、赛道/话题研究、竞品拆解、电商/本地生活视频分析、事实核查、自动搜索素材, save Douyin content as Markdown/TXT, or use subtitle/local ASR as the factual spine with logged-in Douyin Web built-in AI / Doubao fallback as visual or quick-reading supplements.

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 has positioning and wants a reusable Xiaohongshu topic/title bank instead of asking AI to freely brainstorm, especially to map topics to user needs such as emotional resonance, growth proof, new insight, learning value, or identity expression. Trigger phrases: "选题库", "标题公式", "每天发什么", "批量选题", "爆款需求", "XHS topic bank", "title formulas". Do not use before account positioning and target audience are known.

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 has positioning and wants a reusable Xiaohongshu topic/title bank instead of asking AI to freely brainstorm, especially to map topics to user needs such as emotional resonance, growth proof, new insight, learning value, or identity expression. Trigger phrases: "选题库", "标题公式", "每天发什么", "批量选题", "爆款需求", "XHS topic bank", "title formulas". Do not use before account positioning and target audience are known.

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。

Expert guidance for Comet ML, the platform for tracking machine learning experiments, managing models, and monitoring production ML systems. Helps developers log experiments, compare model versions, and build reproducible ML pipelines with automatic code/data versioning.

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。

ClearML is an open-source MLOps platform that records machine-learning experiments, versions datasets, chains tasks into pipelines and runs them on remote machines through agents and queues. Use when a user asks to "track experiments with ClearML", "log metrics, artifacts and models", "version a dataset", "run training on a remote GPU with clearml-agent", "build a ClearML pipeline", "run hyperparameter optimization", or "self-host ClearML Server". Covers the clearml 2.x Python SDK, clearml-agent 3.x and ClearML Server 2.x.

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。

Expert in running effective incident postmortems. Covers blameless analysis, root cause investigation, action item prioritization, and building a learning culture. Understands that incidents are opportunities to improve systems, not punish people. Use when "postmortem, incident review, what went wrong, root cause, blameless, outage, post-incident, " mentioned.

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。

Production incident response - from detection through resolution to post-mortem. Effective communication, systematic investigation, and blameless learning from failuresUse when "incident, outage, production issue, site down, on-call, post-mortem, war room, severity, pages, alerts, rollback, incident, outage, on-call, post-mortem, production, reliability, SRE, communication" mentioned.

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。

Kubernetes is the operating system for the cloud. It schedules containers, handles networking, manages storage, and keeps your apps running. But K8s has a steep learning curve - abstractions on abstractions on abstractions. This skill covers the core concepts (Pods, Deployments, Services), production patterns (health checks, resource limits, HPA), and the gotchas that catch everyone (namespace confusion, service discovery, secret management). 2025 reality: You probably don't need to run your own cluster. Use managed K8s (EKS, GKE, AKS) unless you have a very good reason not to. Focus on writing good manifests, not managing etcd. Use when "kubernetes, k8s, kubectl, deployment manifest, helm chart, kustomize, pod, service yaml, ingress, horizontal pod autoscaler, hpa, kubernetes, k8s, containers, orchestration, devops, cloud-native, helm, kustomize" mentioned.

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。

Expert in course platform selection and implementation - comparing Teachable, Kajabi, Thinkific, and custom solutions. Covers LMS selection criteria, platform migrations, tech stack decisions, and building vs buying. Use when "course platform, lms, teachable, kajabi, thinkific, course hosting, learning management system, which platform, " mentioned.

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。

Angular is opinionated and comprehensive - it gives you everything: routing, forms, HTTP, dependency injection, testing. The learning curve is steep, but once you're in, you move fast. The structure it enforces is why enterprises love it. This skill covers Angular 17+, standalone components, signals, the new control flow syntax, and modern Angular patterns. Key insight: Angular's power is in its DI system and RxJS integration. Master those, and everything else follows. 2025 lesson: Standalone components are the future. NgModules aren't going away, but new projects should start standalone. Signals are Angular's answer to fine-grained reactivity - learn them. Use when "angular, angular component, angular service, angular routing, angular forms, rxjs, ngrx, angular signals, standalone component, angular ssr, angular, typescript, frontend, spa, enterprise, rxjs, signals, standalone" mentioned.

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。

Building AI-powered personalization systems: recommendation engines, collaborative filtering, content-based filtering, user preference learning, cold-start solutions, and LLM-enhanced personalized experiences. Use when "recommendation system, personalization, collaborative filtering, content-based filtering, user preferences, recommend, suggestions, for you, similar items, you might like, " mentioned.

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。

Mastery of AI image generation across the full spectrum: Midjourney for aesthetic perfection, Flux for prompt adherence, DALL-E 3 for concept clarity, Stable Diffusion for control, and Imagen 3 for photorealism. This skill transforms text into visual reality at the speed of thought. We've moved beyond "AI can make pictures" to "AI is the fastest concept artist, product photographer, and visual designer ever created." The skill isn't just prompting—it's understanding how each model thinks, what makes images work, and how to systematically produce exactly what you envision. The best AI image generators have internalized composition, lighting, color theory, and style—not by studying art, but by generating thousands of images and learning what works. They're visual directors who happen to work in text. Use when "AI image, generate image, Midjourney, DALL-E, Flux, Stable Diffusion, Imagen, text to image, AI art, AI photo, AI illustration, AI visual, AI graphics, generate picture, ai-image, midjourney, dall-e, flux, stable-diffusion, imagen, generation, text-to-image, visual, art" mentioned.

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。

Google and Meta paid media account structure evaluation. Audits campaign/ad set architecture against conversion volume minimums, budget thresholds, and targeting overlap. Identifies over-segmentation, under-segmentation, budget fragmentation, and structural anti-patterns blocking algorithmic learning. Provides consolidation roadmaps with migration plans. Use when inheriting accounts, quarterly health checks, or before scaling budgets.

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。

Captures reusable knowledge after work is done — learnings from feedback or failures, resolved bugs (symptom, root cause, prevention), and recurring code patterns — into the project's persistent memory file. Use after feedback, a non-trivial bug fix, a failed attempt, or the third repetition of a pattern.

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。

AWT — AI-Powered E2E Testing (Beta) workflow skill. Use this skill when the user needs 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 and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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。