「wants」の検索結果

Capture a website and create a HyperFrames video from it. Use when: (1) a user provides a URL and wants a video, (2) someone says "capture this site", "turn this into a video", "make a promo from my site", (3) the user wants a social ad, product tour, or any video based on an existing website, (4) the user shares a link and asks for any kind of video content. Even if the user just pastes a URL — this is the skill to use.

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。

Codex-native batch Amazon keyword research using LaunchFast MCP. Use when the user wants a ranked comparison across many keywords and optionally wants HTML and CSV artifacts written to disk.

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。

Codex-native supplier sourcing and outreach workflow for Alibaba using LaunchFast research. Use when the user wants supplier shortlists, outreach messages, reply triage, or negotiation support. Requires `supplier_research`. Browser automation is optional but useful when the user wants Codex to interact with Alibaba directly.

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。

Orchestrate Xcode build optimization by benchmarking first, running the specialist analysis skills, prioritizing findings, requesting explicit approval, delegating approved fixes to xcode-build-fixer, and re-benchmarking after changes. Use when a developer wants an end-to-end build optimization workflow, asks to speed up Xcode builds, wants a full build audit, or needs a recommend-first optimization pass covering compilation, project settings, and packages.

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。

Track what key opinion leaders (KOLs) in your space are posting on LinkedIn and Twitter/X. Surfaces trending narratives, high-engagement topics, and early signals of emerging conversations before they peak. Chains linkedin-profile-post-scraper and twitter-mention-tracker. Use when a marketing team wants to ride trends rather than create them from scratch, or when a founder wants to know which topics are resonating with their audience.

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。

Register, add, or update existing custom Simulink block libraries and configure block policy and knowledge index. Use when a user wants to register, set up, configure, or add existing .slx library files for agent-assisted model building. Do NOT use when the user wants to create or author a new library from scratch — that requires building-simulink-models.

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, validate, and plan a startup from scratch. Covers market research, competitive analysis, business model, brand identity, product definition, financial projections, and validation experiments. Trigger when the user has a startup idea to explore, wants to validate a business concept, needs a business plan or lean canvas, asks for market sizing or competitive landscape, wants brand positioning or go-to-market strategy, or says anything like "I have an idea for..." or "is this idea worth pursuing". Also handles resuming from a previous checkpoint.

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。

This skill should be used when the user asks for a "GPT second opinion", wants a "cross-model review", needs to "check a plan before committing", wants another view after repeated failures, or explicitly invokes "codex-advisor".

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 any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .xltx, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

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。

Trigger when: (1) User wants to create an educational/explainer video, (2) User has a vague concept they want visualized, (3) User mentions "3b1b style" or "explain like 3Blue1Brown", (4) User wants to plan a Manim video or animation sequence, (5) User asks to "compose" or "plan" a math/science visualization. Transforms vague video ideas into detailed scene-by-scene plans (scenes.md). Conducts research, asks clarifying questions about audience/scope/focus, and outputs comprehensive scene specifications ready for implementation with ManimCE or ManimGL. Use this BEFORE writing any Manim code. This skill plans the video; use manimce-best-practices or manimgl-best-practices for implementation.

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 to classify tabular data end-to-end in MATLAB — load a dataset, prepare and clean it, select promising classifiers, train them, and compare accuracies with cross-validation, holdout, or hyperparameter optimization plus statistical tests. TRIGGER when: user asks to classify tabular data, pick classifiers for a dataset, compare classifier accuracy, run cross-validation or a holdout evaluation, or find the best model with statistical uncertainty. DO NOT TRIGGER when: user has non-tabular inputs (images, sequences, time series), wants a regression model, is training a specific neural network architecture (use matlab-train-network), or wants cost-sensitive learning or an arbitrary class-prior vector (this skill only supports the built-in uniform-prior toggle for imbalanced data).

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。

Share MATLAB content by guiding users through uploading to GitHub, MATLAB Drive, or File Exchange, then generating "Open in MATLAB Online" URLs. Covers the full sharing workflow: choosing a platform, uploading content (automated via gh CLI for GitHub, manual for MATLAB Drive and File Exchange), and constructing the correct URL. Use when a user wants to share MATLAB code with others, open local files in MATLAB Online, generate an open-in-MATLAB-Online button or badge, or when an AI agent has generated MATLAB code locally and the user wants to share it or run it in MATLAB Online.

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, modify, or delete experiments in Experiment Manager with live UI sync. TRIGGER when: user asks to create an experiment from code/script, wants to sweep parameters, asks to add/delete/edit experiments, or wants parameter changes reflected in the UI. Combines experiment creation (from code analysis) with frontend UI synchronization.

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。

CloudBase declarative deployment from a cloudbaserc config (声明式部署, 配置式部署, cloudbaserc 部署) through the deployBuild / deployPlan / deployApply MCP tools. Use when deploying database, functions, app, hosting, or gateway resources described in cloudbaserc.json/yaml as a single desired-state config, when a user wants to build static hosting artifacts locally first (deployBuild), or wants a dry-run plan before applying (deployPlan), or when handling multi-environment deploys via mode / envOverrides. Covers build-plan-apply flow (deployBuild local build → deployPlan dry-run → deployApply confirm=true), hosting build-output neutralization, envId resolution priority, only/skip filtering, concurrency, and continueOnError. Prefer deployBuild (when hosting declares a buildCommand) and deployPlan before deployApply; do not confuse with per-resource tcb CLI deploy or single-function deploy.

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 operator wants a hero or meta image for a Prisma blog post; asks to create or generate a blog hero, cover, social card, Open Graph, or YouTube image; mentions cover art, a blog thumbnail, cover.svg/hero.svg/meta.png; references content-create-hero-image; or wants to interactively design cover imagery in Prisma's 2026 brand (light paper, prism accents, Sora). Produces an editable SVG hero plus a pixel-exact PNG meta image, and includes an interactive mode and a built-in design-review pass.

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 complete SEO setup for local business websites — HTML head tags, JSON-LD LocalBusiness schema, robots.txt, sitemap.xml. Australian-optimised with +61 phone, ABN, suburb patterns. Use whenever the user wants SEO for a local business (tradesman, café, clinic, agency, retailer), needs JSON-LD structured data, asks for LocalBusiness schema, or wants meta tags / robots.txt / sitemap for a suburb-serving business.

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 to find YouTube content on any topic: searching for videos or channels, finding creators who cover a subject, discovering tutorials, talks, or expert discussions, or looking up a channel by name or handle. Also use proactively when the user wants to research a topic and YouTube is a good source. Not for account management or written-source-only research.

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。

Automatically invoke this skill whenever the user asks why a Fabric capacity is busy, throttled, at 90 or 100 percent, or what is consuming CUs; asks for current capacity utilization, carry forward or per-item CU usage; wants to query or repair the Microsoft Fabric Capacity Metrics app or its semantic model; or wants to start, pause or monitor a capacity from a script or status bar.

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 build complete web experiences from scratch — award-level product and marketing pages, cinematic scroll-directed sites where the page is directed like a film, and multi-screen products (app, dashboard, admin, settings, onboarding, docs site) built as one design system with every component, state and route in one style. Use whenever the user wants to create or redesign a landing page, website, hero section, portfolio, promo or product page; wants scroll animations, storytelling pages, or a site that feels like a movie; or says "make it beautiful", "make it wow", "cinematic", or "design system" — even if they name no technique. Generates its own assets via local CLIs (images, video frames, depth maps, 3D), and gates every ship with an executable anti-slop linter and a screenshot verification loop. Not for polishing a UI someone else built, and not for backend-only 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。

When the user wants to analyze Google Search Console data, use the GSC API, or interpret search performance. Also use when the user mentions "GSC," "Search Console," "indexing report," "Core Web Vitals," "Enhancements," "Insights report," "search performance," "search queries," "search performance report," "URL inspection," "impressions," "CTR," "average position," "index coverage," "GSC data analysis," "Search Console API," or "searchanalytics.query." When the user wants to rewrite title tags (not only report on them), use title-tag. For meta description rewrites, use meta-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。

Compare several Japanese NLP libraries, models, or datasets for a keyword (a specific tool name, or a function/task like '形態素解析') across a handful of criteria chosen for that comparison, rendered as a ○/△/✕ table. Use when the user wants a side-by-side comparison of multiple Japanese NLP tools/libraries/datasets, not just the single best one. Trigger phrases include 'X と Y と Z を比較して', '形態素解析ライブラリを比較', 'MeCab と Sudachi どっちがいい', 'どのツールを使うべき', 'compare japanese tokenizers', 'mecab vs sudachi vs janome', 'which embedding model should I use', '日本語NERライブラリの比較表', 'pros and cons of japanese OCR tools'. For a single ranked list use search; for alternatives to one specific tool (or contribution candidates) without a multi-axis table, use discover.

Search all Japanese NLP resources (libraries, models, datasets, tutorials, dictionaries, Hugging Face). Accepts keywords or natural language questions in any language. Use whenever the user asks which Japanese NLP resource to use, or wants to find one: tokenizers / morphological analyzers, BERT or LLM models, embeddings, NER, text classification, datasets / corpora, dictionaries, tutorials, or Hugging Face models. Trigger phrases include '日本語の形態素解析ライブラリ', 'おすすめの日本語tokenizer', '日本語BERTモデル', '日本語の感情分析データセット', '日本語LLM 一覧', 'which Japanese embedding model', 'Japanese NER library'.