3,834 件 ・ 関連度順
概要と使いどころ
Assemble a submission-ready writer package from final method, result, robustness, figure, and human-decision artifacts, then freeze approved numerical claims with provenance.
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 and maintain global and method-specific mathematical-model assumptions from the problem frame, active method cards, data profile, and risk probes, while leaving necessity and impact judgments to the human modeler.
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。
Detect whether approved modeling code is Python or MATLAB/Beita Tianyuan and route it to the matching reviewer using the compact named-check review contract.
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。
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。
Run scoped or final cross-media consistency checks for mathematical-modeling artifacts, comparing canonical numbers, symbols, parameters, decisions, files, and paper claims without performing full-workspace audits for low-risk changes.
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。
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。
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。
Generate and render-verify publication-quality mathematical-modeling figures from saved evidence, using the approved figure plan, source data, claim, type, and consistent visual system.
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。
Map contest attachments to subquestions, audit and clean raw data, and emit one reusable data profile with quality, coverage, imbalance, concentration, and method-readiness evidence for downstream risk screening.
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 a human-approved main method and usable baseline into a minimal language-neutral implementation and experiment contract. Use after G2.5 and data readiness, before Python or MATLAB code generation.
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 maintain one global mathematical symbol and unit table from the problem frame and active method cards, resolving cross-subquestion conflicts before code or paper 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。
Build the authoritative final method explanation for a submission-ready subquestion from the method card, human decision ledger, code plan, final results, and robustness evidence.
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 one compact choice card at a genuine mathematical-modeling judgment point. Use before method screening, after a meaningful experiment, or before final claim/freeze approval so the human chooses the trade-off while AI handles mechanical consequences.
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。
Generate and run minimal reproducible Python modeling code for the human-approved main method and usable baseline, saving compact experiment artifacts and a canonical run summary.
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。
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。