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概要と使いどころ
Integracao completa com Amazon Alexa para criar skills de voz inteligentes, transformar Alexa em assistente com Claude como cerebro (projeto Auri) e integrar com AWS ecosystem (Lambda, DynamoDB, Polly, Transcribe, Lex, Smart Home).
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。
Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.
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。
Guarantee valid JSON/XML/code structure during generation, use Pydantic models for type-safe outputs, support local models (Transformers, vLLM), and maximize inference speed with Outlines - dottxt.ai's structured generation library
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。
Fetch YouTube video transcripts and transform them into structured content (chapters, summaries, threads, blog posts). Use when the user shares a YouTube URL or video link, asks to summarize a video, requests a transcript, or wants to extract and reformat content from any YouTube video.
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。
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.
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 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.
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。
Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential 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。
根据指定的行切片规则和列映射，将源DataFrame（df_sub）的列数据转换为长格式DataFrame，包含sub_id、trial、start_time、end_time等字段。
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。
在对具有多层索引的Pandas DataFrame进行分组计算时，确保返回的结果保留原始的完整索引结构，而不是仅保留分组键。
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。
Assists in writing high-quality content by conducting research, adding citations, improving hooks, iterating on outlines, and providing real-time feedback on each section. Transforms your writing process from solo effort to collaborative partnership.
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。
Transform data into compelling narratives using visualization, context, and persuasive structure. Use when presenting analytics to stakeholders, creating data reports, or building executive presentations.
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 integrating third-party APIs with proper authentication, error handling, rate limiting, and retry logic. Use when integrating REST APIs, GraphQL endpoints, webhooks, or external services. Specializes in OAuth flows, API key management, request/response transformation, and building robust API clients.
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。
Intelligent pattern selection for Fabric CLI. Automatically selects the right pattern from 242+ specialized prompts based on your intent - threat modeling, analysis, summarization, content creation, extraction, and more. USE WHEN processing content, analyzing data, creating summaries, threat modeling, or transforming text.
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。
Professional Ziwei Doushu consultation skill with offline, Beijing-standard calculation rules. Use when the user wants a polished Ziwei report from birth date and time, including life palace structure, twelve palaces, four transformations, major luck cycles, yearly triggers, optional chart images, and a clear split between chart facts and traditional 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。
Enterprise and solution architecture — system design, scalability, cloud strategy, digital transformation, data architecture, governance, technology strategy. Practitioner-level TOGAF, C4, ArchiMate, Zachman, Wardley mapping and ADR authoring.
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 this skill when the user needs to transform business requirements, use case descriptions, or meeting transcripts into a technical Power Platform solution architecture, including component selection and Mermaid.js diagrams.
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。
Convert Markdown files to HTML similar to `marked.js`, `pandoc`, `gomarkdown/markdown`, or similar tools; or writing custom script to convert markdown to html and/or working on web template systems like `jekyll/jekyll`, `gohugoio/hugo`, or similar web templating systems that utilize markdown documents, converting them to html. Use when asked to "convert markdown to html", "transform md to html", "render markdown", "generate html from markdown", or when working with .md files and/or web a templating system that converts markdown to HTML output. Supports CLI and Node.js workflows with GFM, CommonMark, and standard Markdown flavors.
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 this skill whenever the user wants to build scroll animations, scroll effects, parallax, scroll-triggered reveals, pinned sections, horizontal scroll, text animations, or any motion tied to scroll position — in vanilla JS, React, or Next.js. Covers GSAP ScrollTrigger (pinning, scrubbing, snapping, timelines, horizontal scroll, ScrollSmoother, matchMedia) and Framer Motion / Motion v12 (useScroll, useTransform, useSpring, whileInView, variants). Use this skill even if the user just says "animate on scroll", "fade in as I scroll", "make it scroll like Apple", "parallax effect", "sticky section", "scroll progress bar", or "entrance animation". Also triggers for Copilot prompt patterns for GSAP or Framer Motion code generation. Pairs with the premium-frontend-ui skill for creative philosophy and design-level polish.
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。
Transform this Python script into a polished, beginner-friendly project by refactoring the code, adding clear instructional comments, and generating a complete markdown tutorial.
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。
Automatisation de la création de modèles 3D via Meshy.ai. Utilisez cette compétence pour transformer du texte ou des images en modèles 3D (STL/OBJ), appliquer des textures et livrer le fichier final.
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。
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.
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。
Creates step-by-step tutorials and educational content from code. Transforms complex concepts into progressive learning experiences with hands-on examples. Use PROACTIVELY for onboarding guides, feature tutorials, or concept explanations.
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。