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概要と使いどころ
Summarize modeling experiment evidence, compare the approved main method with a usable baseline, surface fallback triggers, and produce a decision-point or final report without creating routine per-round prose.
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 and risk-screen a compact role-based method shortlist for a mathematical-modeling subquestion. Use after problem framing and data profiling, before model code generation, to propose a main candidate, a usable baseline, and at most one conditional fallback without padding the pool.
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。
Review, run, debug, and verify approved Python modeling code against its code plan, data contract, method decision, risk conditions, and experiment outputs, saving one compact JSON review.
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。
Manage and verify references for mathematical modeling contest papers, generating BibTeX entries, checking citation completeness, and ensuring all references are traceable to actual sources.
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。
Query Fabric lakehouse and warehouse data using DuckDB, either locally or inside a Fabric notebook. Automatically invoke when the user mentions "DuckDB", "query Delta tables locally", or asks to "attach DuckDB to a lakehouse", "query OneLake data", "explore lakehouse data", "data freshness check", "validate data quality", "use DuckDB in Fabric".
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 plan SEO strategy, prioritize SEO work, or understand the SEO workflow. Also use when the user mentions "SEO strategy," "SEO plan," "SEO roadmap," "SEO priority," "SEO audit," "SEO workflow," "where to start SEO," "SEO approach," "organic growth strategy," "why SEO," "SEO value," or "search strategy." For technical/crawl audit execution, use seo-audit. For keyword research, use keyword-research. For AI search visibility, use generative-engine-optimization.
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。
Use when generating video with Vidu models on Scenario via MCP: text-to-video, image-to-video from a single still, start and end frame interpolation, reference-to-video keeping characters and props consistent from up to 7 reference images or reference videos, anime-style motion, movement amplitude control, or deciding between Q3, Q2, Q1, and 2.0 tiers. Keywords: Vidu Q3 Pro, Q2 Turbo, Shengshu Technology, T2V, I2V, R2V, Reference2V, start end frame, background music toggle.
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 a task involves game textures or materials through the Scenario MCP: seamless or tileable textures, themed texture packs (brick, wood, stone, floors, hand-painted), PBR materials (albedo, metallic, roughness, normal) on 3D assets, retexturing a mesh, material iteration from reference images, texture upscaling that must preserve tiling, or sizing textures for game engines. Keywords: seamless texture, tileable, PBR, material, retexture, texture upscale, surface.
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 retexturing an existing 3D asset with PATINA PBR materials through the Scenario MCP server: a GLB or Blender scene (an image-to-3D result with flat colors, a prop, a diorama) whose surfaces need basecolor, normal, roughness, metalness, and height maps per material family with geometry untouched, or a matched before-and-after comparison film rendered in Blender with one camera path, lighting, and cut. Keywords: PATINA, PBR retexture, material families, Blender EEVEE, comparison 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。
Define the smallest first version worth building (the MVP scope) from a PRD through a guided interview, with the AI Product Development Toolkit's MVP scope prompt.
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 the MVP build from a PRD and an MVP scope, as a full development plan or an ultra-lean build spec, with the AI Product Development Toolkit's planning prompts.
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 PRD into UX specifications (screens, user flows and interaction patterns) through a guided interview, with the AI Product Development Toolkit's UX prompt.
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 a user research questionnaire, or turn collected responses into evidence-linked input for a PRD, with the AI Product Development Toolkit's research prompts.
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。
Make a short video about a product or brand (promo, launch, explainer, demo or social clip) entirely in code with Remotion, from idea to a rendered MP4 with sound. The owner approves the idea and a storyboard before anything is built.
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。
Set up the current empty folder with the AI App Starter, so the agent builds a Next.js, Supabase and Vercel app with the owner step by step, from idea to launch.
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 owner about a product idea, then draft a Product Requirements Document (PRD) with the AI Product Development Toolkit's PRD prompt.
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。
Draft a test plan outline (scope, test types, environments, and entry and exit criteria) through a guided interview, with the AI Product Development Toolkit's test plan prompt.
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 a 2-3 line pitch from the user, draft a product overview (PRD.md) and a technical design (ARCHITECTURE.md) for a third-party `.ppmplugin` native control, then walk through every operation's iOS + Android implementation strategy with opinionated recommendations (library choice, hosting, key APIs, edge cases) and capture the agreed spec in ARCHITECTURE.md §3.<n>. The depth of ARCHITECTURE.md is what lets the scaffold skill generate complete working code instead of TODO placeholders. Iterates with the user until they approve both docs. Optionally seeds from a design doc / FRD URL. PRD.md + ARCHITECTURE.md are the source of truth for every downstream skill (generate, build, assemble). Run this BEFORE any code is generated.
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。
Navigate CNKI search result pages (next/previous/specific page) or change sort order. Use when user wants to see more results or change sorting.
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。
Query journal indexing/inclusion status on CNKI - check which databases include a journal (北大核心, CSSCI, CSCD, SCI, EI, etc.), get impact factors and evaluation data. Use when user asks about a journal's level, indexing, or ranking.
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。
Browse journal issues, view table of contents, and download original TOC PDF from CNKI. Use when user wants to see papers in a specific journal issue or download the original cover/TOC pages.
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。
Parse current CNKI search results page into structured paper data (title, authors, journal, date, citations). Use after a search has been performed and you need to extract the results.
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。
Extract full paper details from a CNKI paper page including title, authors, affiliations, abstract, keywords, fund, classification. Use when the user needs detailed information about a specific 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。