1万 件 ・ 関連度順
概要と使いどころ
Draft submission-ready mathematical-modeling paper sections from the approved solution package, frozen numbers, human decision ledger, and verified figures without searching scattered exploratory outputs or inventing 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。
Polish mathematical modeling paper drafts for grammar, clarity, formula consistency, hedging calibration, overclaim detection, and contest formatting compliance. Use after paper-section-writer has drafted sections.
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。
Classify each parsed mathematical-modeling subquestion by required output and structure, surface ambiguous framing trade-offs for human choice, and record primary/secondary task types without selecting algorithms.
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。
Perform the final submission-level audit of mathematical-modeling workflow integrity, evidence quality, anti-fabrication, paper coherence, figures, references, and contest readiness after consistency and completeness audits 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。
Parse a mathematical-modeling problem into goals, objects, data, constraints, outputs, subquestions, dependencies, variables, relationships, and human-confirmed success criteria before any method selection.
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。
Package a G6-approved mathematical-modeling paper and its exact supporting code, data, results, and figures without changing their behavior or the workspace.
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。
Inspect a mathematical-modeling workspace, evaluate lean or submission gates per subquestion, update machine-readable manifests, classify change impact, and route one next action without duplicating downstream 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。
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。
Review, run, debug, and verify approved MATLAB or Beita Tianyuan modeling code against its plan, data contract, method decision, compatibility constraints, 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。
Collect and analyze relevant papers, reports, and reference methods to inform method selection without fabricating references or copying models blindly.
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 to refresh a semantic model or a dataset. Can also be used to manage, optimize, troubleshoot, or configure a refresh or a refresh schedule.
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 whenever the user mentions "pbir", "pbir-cli", "Power BI reports", or "PBI reports", works with .pbir, .pbip, or .pbix files, or wants to refresh, screenshot, or visually verify a report that is open in Power BI Desktop. Covers creating, exploring, formatting, validating, and publishing Power BI reports through the pbir CLI and object model, plus driving Power BI Desktop (canvas reload, page screenshots) and querying connected or local semantic 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。
Expert guidance for the cross-platform Tabular Editor CLI (the `te` binary, currently in preview) that manages Power BI / Analysis Services semantic models from the terminal on macOS, Linux, and Windows. Use when the user mentions the `te` CLI or "Tabular Editor CLI" (not the "2"), or runs a `te <command>` to scaffold, inspect, edit, validate, run BPA on, query, deploy, refresh, test, or migrate a semantic model. Not for the legacy Windows-only `TabularEditor.exe` (TE2).
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 text brief or a reference image must become a Blender blockout whose playblast guides a render-to-real video pass (a video model that takes a reference clip, such as Seedance, or the Render Video lane of the Scenario Blender Plugin): graybox or previs built for a video model, color-coded proxies at metric scale from rough to detailed, density levels from a simple shot to an expert scene with crowds, traffic and many cuts, proxy figures, a camera locked to the timeline, rail or tracking camera rigs, lens per shot, framing and occlusion checks, a playblast, a hand-off sheet for the video prompt. Also when a render-to-real pass renders the proxies as boxes, reads one figure as two people, swaps a car's front and rear, drifts at the end, or the guide camera pops on a cut.
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 modeling hard surface in ZBrush 2026 (sci-fi helmet, weapon, prop, armor plates, mechanical parts, kitbash, vents, panel lines, bolts, concept to game asset), when using ZModeler, Dynamic Subdivision and creasing, Live Boolean, Knife, Clip, Slice or Trim curves, IMM, ArrayMesh, NanoMesh or Panel Loops, or when edges look razor sharp, a Make Boolean Mesh fails or leaves holes, planes look wobbly, Mirror And Weld erased an edit, or ZRemesher ruins a hard-surface part.
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 or editing video with Seedance models on Scenario via MCP: text-to-video, image-to-video, first and last frame anchors, reference-to-video from identity, product, or world references, prompt-based editing, extending a clip, audio-conditioned motion or voice, shot sound without music, first-frame versus reference conditioning, or an output blocked by moderation. Keywords: Seedance 2.5 and 2.0, ByteDance, T2V, I2V, V2V, native audio, OutputVideoSensitiveContentDetected.
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 creating or refining 3D assets with Meshy models on Scenario via MCP: image-to-3D from one photo or 1-4 multi-view angles, text-to-3D, retexturing an existing GLB, remeshing to a target polycount, UV unwrapping, auto-rigging a humanoid character, or applying a library animation clip. Keywords: Meshy 7 and Meshy 6, Ultra mode, Smart Topology, PBR maps, texture prompt, triangle or quad topology, game-ready mesh, A-pose, T-pose, GLB pipeline.
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 or editing images with Luma Uni-1 models on Scenario via MCP: text-to-image, prompt-based editing of an existing image, style or character reference images with named roles, web search grounding for real-world subjects, rendering exact title text into posters, aspect ratio control, or choosing between Uni-1 Max and Uni-1. Keywords: Luma Labs, Uni-1, Photon, txt2img, img2img, image edit, reference images, webSearch, reasoning image model.
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 or editing images with Scenario through MCP: text-to-image, image-to-image, instruction editing, inpainting or outpainting with a mask, background control, aspect ratio or resolution sizing, several outputs per run, or choosing between Scenario image models. Also when a run fails on prompt length, a plan-restricted model, or a reference image that was silently ignored. Keywords: txt2img, img2img, image edit, inpaint, mask, reference image, aspect ratio.
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 one look must hold across Scenario generations: one character across scenes, a turnaround, or a video animated from its references, one product across angles, one style across icon sets, a character from an uploaded drawing, a variant off an approved baseline, or deciding when references stop scaling and a trained model is due. Triggers: make this match, same character, on-model, reference to video, style reference. Keywords: consistency, identity, control map, seed, LoRA.
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。
Analyze current trends and challenges in Japanese NLP for a topic. Surveys the existing awesome-japanese-nlp-resources dataset and augments it with up-to-the-minute web research to produce a combined trend + issue report. Use only when the user explicitly wants a trend/landscape report, a challenges/limitations report, or a general research overview of a Japanese NLP topic (this combines the bundled dataset with live web research). Trigger phrases include '日本語LLMの最新トレンド', '〜の動向をまとめて', '最近の日本語NLPの流れ', '日本語LLMの課題', '〜の問題点・限界', '未解決の論点', 'trend report on Japanese embeddings', 'latest Japanese speech models', 'challenges in Japanese NER', 'limitations of Japanese embeddings'. For a simple lookup use the search skill; this one runs web research.
Integrates Power Pages generative-AI summarization APIs (PREVIEW) into a Single Page Application (SPA) site — the Search Summary API and the Data Summarization API — on any record-detail or list page. Generates per-target service code (CSRF-handled) and AI site settings; delegates Web API settings, table permissions, and web roles to `/integrate-webapi` and `/create-webroles`. Use whenever a user wants AI/Copilot output that condenses Dataverse content on a Power Pages site — an AI summary, AI-generated overview or "key insights" across a record or list, a search-results summary, a case/incident summary, or recommendation-chip refinement — even when phrased as "AI-generated paragraph", "insights", or "overview". Do NOT use for: generative pages in model-driven apps (use the model-apps `genpage` skill), Copilot Studio agents/chatbots, summarizing documents or PDFs, Power BI dashboards, plain keyword search with no AI summary, or plain Dataverse CRUD (use `/integrate-webapi`).
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 design or redesign the Dataverse schema and connector plan for an existing mobile app, or has an ER diagram (image, Mermaid, or text) to apply. Skip when the user is creating a brand-new app — /create-mobile-app handles the data model inline.
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。