「set」の検索結果

NotebookLM MCP server management - query notebooks, add from share links, handle auth, reset sessions. Use when working with Google NotebookLM notebooks for conversational research 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。

Serverless form handling on Netlify-hosted sites — detects HTML forms at deploy time, stores submissions, filters spam, and sends notifications. Use when adding a contact form, lead-capture form, file-upload form, or newsletter signup to a Netlify site; wiring AJAX form submission; setting up a custom thank-you page; adding a honeypot or reCAPTCHA to a form; getting forms working in Next.js, Nuxt, SvelteKit, Astro, or Gatsby; reading form submissions via the Netlify API; or debugging missing submissions and forms that silently fail to register.

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。

Write, configure, and deploy Netlify serverless functions in TypeScript, JavaScript, or Go. Use this when adding an API endpoint or backend route, adding a contact form handler, wiring auth or Identity signup/login hooks, building streaming or AI-proxy responses, scheduling cron jobs, running long background jobs (batch processing/scraping), reacting to deploy or form events, setting up rate limiting or region/memory config, or reading environment variables and secrets inside a function. Covers file locations, the Request/Context/Response handler shape, path routing, config options, and local testing with netlify dev.

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。

Cache dynamic and static responses on Netlify's CDN from Functions, Edge Functions, and proxies. Use when you add caching or cache-control headers to a function response, tune cache TTL or stale-while-revalidate, set up the durable cache, vary a cache key by query/header/cookie/country/language, purge or invalidate the cache by site or cache tag, use the programmatic Cache API (caches.open/match/put) or @netlify/cache helpers (fetchWithCache/cacheHeaders/getCacheStatus), speed up an expensive API call, add ISR or on-demand revalidation, or debug why a response is or isn't cached via the Cache-Status header.

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。

Store and retrieve unstructured objects, file uploads, and cache-like state on Netlify using the @netlify/blobs key/value API from Functions, Edge Functions, and Build Plugins. Use when a task involves saving user file or image uploads, persisting form or contact-form submissions, storing generated output from Background Functions (sitemaps/processed media/bulk-email results), building read-only asset stores, adding client-side blob expiration, or wiring file-based blob uploads at deploy time. Not for per-user, transactional, or relational data (counters/balances/sessions) — reach for Netlify DB there instead.

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。

Zero-config Postgres for Netlify apps via @netlify/database — querying data from Functions/Edge Functions, writing schema migrations, setting up Drizzle ORM, local dev with netlify dev, database branches for deploy previews, and migrating an existing Postgres project onto Netlify. Use when adding a database, building a contact form or CRUD API, writing SQL migrations, wiring up Drizzle, running netlify database commands, testing with a local Postgres, or switching from Neon/Supabase/RDS to Netlify Database.

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 OpenAI, Anthropic, Google Gemini, or OpenRouter models from Netlify Functions or Edge Functions without managing provider API keys or accounts — the gateway injects credentials automatically. Reach for this when you add an AI chatbot or completion endpoint, generate images or text with Gemini/GPT/Claude, summarize form submissions with AI, build an LLM-backed API route, stream a long AI generation, or wire up any server-side AI provider call on Netlify. Covers provider SDK setup, injected env vars, model availability, rate limits, credit costs, streaming for long generations, and local dev with netlify dev or the Vite plugin.

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。

Picks the right Netlify protection layer for a deployed site and disambiguates the three unrelated things people call "auth". Use when a developer wants to password-protect a site or previews, restrict a project to their team, make a project public/private, set team visibility defaults, require SSO to view a site, or debug SSO-session symptoms like being logged out mid-session / getting 401s on an SSO-protected site / token expiry or refresh. Routes app-user login ("who is this user in my app") to the netlify-identity skill and dashboard/team SSO SSO elsewhere; this skill only chooses the perimeter layer for site/preview access.

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。

MANDATORY prerequisite — load this skill BEFORE every `generate_diagram` tool call. NEVER call `generate_diagram` directly without loading this skill first. Trigger whenever the user asks to create, generate, draw, render, sketch, or build a diagram — flowchart, architecture diagram, sequence diagram, ERD or entity-relationship diagram, state diagram or state machine, gantt chart, or timeline. Also trigger when the user mentions Mermaid syntax or wants a system architecture, decision tree, dependency graph, API call flow, auth handshake, schema, or pipeline visualized in FigJam. Routes to type-specific guidance, sets universal Mermaid constraints, and tells you when to use a different diagram type or skip the tool entirely (mindmaps, pie charts, class diagrams, etc.).

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。

Look up and read Hugging Face paper pages in markdown, and use the papers API for structured metadata such as authors, linked models/datasets/spaces, Github repo and project page. Use when the user shares a Hugging Face paper page URL, an arXiv URL or ID, or asks to summarize, explain, or analyze an AI research paper.

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。

Publish and manage research papers on Hugging Face Hub. Supports creating paper pages, linking papers to models/datasets, claiming authorship, and generating professional markdown-based research articles.

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。

Translate Figma designs into production-ready application code with 1:1 visual fidelity. Use for Figma URLs, node IDs, implementation requests, or components that must match Figma specs; use the local `figma` skill for general Figma MCP context and setup.

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。

**MANDATORY prerequisite** — you MUST invoke this skill BEFORE every `use_figma` tool call. NEVER call `use_figma` directly without loading this skill first. Skipping it causes common, hard-to-debug failures. Trigger whenever the user wants to perform a write action or a unique read action that requires JavaScript execution in the Figma file context — e.g. create/edit/delete nodes, set up variables or tokens, build components and variants, modify auto-layout or fills, bind variables to properties, or inspect file structure programmatically.

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 Hugging Face Hub via MCP server tools. Search models, datasets, Spaces, papers. Get repo details, fetch documentation, run compute jobs, and use Gradio Spaces as AI tools. Available when connected to the HF 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。

Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3 — plus any Transformers classifier), and SAM/SAM2 segmentation using Hugging Face Transformers on Hugging Face Jobs cloud GPUs. Covers COCO-format dataset preparation, Albumentations augmentation, mAP/mAR evaluation, accuracy metrics, SAM segmentation with bbox/point prompts, DiceCE loss, hardware selection, cost estimation, Trackio monitoring, and Hub persistence. Use when users mention training object detection, image classification, SAM, SAM2, segmentation, image matting, DETR, D-FINE, RT-DETR, ViT, timm, MobileNet, ResNet, bounding box models, or fine-tuning vision models on Hugging Face Jobs.

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, list, read, message, wait on, fork, rename, archive, and pin Codex threads (sidebar tasks), plus automations and app navigation, using the app-native codex_app tools. Use when the session uses a custom (non-OpenAI) model, for example deepseek-v4-flash or mimo-v2.5, and the user asks to create a thread or a new task or agent, list or read threads, send a message to a thread, wait for a thread, fork or rename a thread, archive or pin a thread, set up an automation or reminder, or open something in the Codex app.

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 doc generation with JSDoc/TSDoc, linters, and pre-commit hooks. Use when setting up markdownlint, configuring doc linting pipelines, integrating JSDoc/TSDoc, or building automated documentation 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。

Refresh a safe, concise map of this GCI World 2026 workspace before deeper course, assignment, or dataset work.

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 en produits digitaux avancé (ebooks, templates, presets, Notion, courses)

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。

Course creator — Curriculum design, video production, LMS setup, pricing, launch strategy

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。

Write and improve prompts for Qwen-Image-2.1, Alibaba's open-weight image generation and editing model. Use whenever the user wants to generate or edit images with Qwen-Image / Qwen-Image-2.1 / Qwen-Image-Edit, asks for help prompting any Qwen image model, needs readable text rendered inside an image (posters, signs, UI mockups, infographics), wants to combine several reference images into one, or asks about aspect ratios, styles, or camera settings for Qwen image generation — even when they are only choosing an image model for a text-heavy graphic and have not named the model yet.

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。

Draw from architecture, fashion, music, food, and industrial design to expand your design taste range. Use when seeking inspiration beyond digital, building diverse reference sets, or expanding creative vocabulary.

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 design quality evaluation. Hierarchy, type, color, space, craft, system. Use when evaluating whether a design is ready to ship, running quality audits, or setting quality standards.

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 a reference system that returns the right reference in under 60 seconds. Use when setting up a design reference library, reorganizing existing collections, or helping teams build shared reference systems.

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。