950 件 ・ 関連度順
概要と使いどころ
Review mathematical modeling competition papers from a complete problem and paper, with an optional strict bzd-paper-format-checker audit, itemized problem-specific scoring, a bottom-up low-score safeguard, and CUMCM award calibration by problem type, division, region, school and advisor. Generate a Chinese HTML judge report.
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Query 2021-2025 CUMCM school awards and 2026 forecasts, or assess a student's preparation distance from provincial and national awards using school history and prior modeling experience.
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 a compliant Chinese mathematical-modeling problem-restatement chapter from a complete problem statement, or review a user's restatement against the original problem. Covers research background, problem review, and research overview; does not perform the separate problem-analysis chapter.
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 a Chinese mathematical-modeling problem-analysis section against the original problem, checking task classification, data reasoning, cross-problem linkage, model-choice rationale, validation planning, chapter boundaries, and the overall solution framework. Use for diagnosis, not automatic full rewriting.
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 a mathematical-modeling paper's model-assumption section against the complete problem statement. Diagnose whether each assumption is necessary, evidence-based, compatible with the task, appropriately simplified, and usable by the later model. Use for assumption review, not automatic full-paper review or unsupported assumption 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。
Review the symbol-notation section of a mathematical-modeling paper against the full paper. Check table coverage, symbol meanings, units, overloading, case and subscript consistency, first-use definitions, formula usage, and visible table formatting. Use for symbol-table diagnosis, not general paper 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。
Review the model establishment, numerical solution, result analysis, model validation, and sensitivity or robustness sections of a mathematical-modeling paper against the complete problem. Use for core-model-section diagnosis, not automatic full-paper scoring or unsupported recomputation.
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。
Execution cost estimation, slippage curve modeling, and optimal trade sizing based on AMM liquidity depth
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。
Intelligent pattern selection for Fabric CLI. Automatically selects the right pattern from 242+ specialized prompts based on your intent - threat modeling, analysis, summarization, content creation, extraction, and more. USE WHEN processing content, analyzing data, creating summaries, threat modeling, or transforming text.
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。
Alias for domain-modeling. Use when the user asks for domain modeling, ubiquitous language, domain terminology, glossary work, or DDD 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。
BRENDA Enzyme DB SOAP/REST queries: kinetic parameters (Km, Vmax, kcat, Ki), EC classes, substrate specificity, inhibitors, cofactors, organism data. 80K+ enzymes, 7M+ values. Free academic registration. For metabolic modeling use cobrapy-metabolic-modeling; metabolites use hmdb-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。
Constraint-based (COBRA) analysis of genome-scale metabolic models: FBA, FVA, knockouts, flux sampling, production envelopes, gapfilling, media optimization. Use for strain design, essential gene ID, flux analysis. For kinetic modeling use tellurium; for visualization use Escher.
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。
Bayesian modeling with PyMC 5: priors, likelihood, NUTS/ADVI sampling, diagnostics (R-hat, ESS), LOO/WAIC comparison, prediction. Hierarchical, logistic, GP variants; predictive checks.
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Python statistical modeling: regression (OLS, WLS, GLM), discrete (Logit, Poisson, NegBin), time series (ARIMA, SARIMAX, VAR), with rigorous inference, diagnostics, and hypothesis tests. Use scikit-learn for ML; statistical-analysis for test choice.
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。
Security audit, hardening, threat modeling (STRIDE/PASTA), Red/Blue Team, OWASP checks, code review, incident response, and infrastructure security for any project.
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Configuring Google Consent Mode v2 for privacy-compliant measurement and advertising. Covers default and update commands, consent state mapping to GA4 and Google Ads, conversion modeling with cookieless pings, and EEA requirements effective March 2024.
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。
Plugin shim for the mathmodel-skill competition workflow. Use when Codex invokes this plugin for a CUMCM, MCM/ICM, or Diangong Cup modeling-contest task, including problem selection, modeling, robustness, contest-paper writing, compliance, or final submission review. Do not use for generic data analysis or non-competition paper 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。
Apply the bioengineering, translational systems biology, and tissue modeling frameworks of Linda Griffith, Professor of Biological Engineering at MIT and tissue engineering pioneer. Use this skill whenever facing decisions, designs, or evaluations in bioengineering, tissue modeling, microphysiological systems (organs-on-chips), synthetic matrix design, disease subtyping, or drug target validation. Reach for this skill when evaluating preclinical models (human vs. animal), reframing overlooked or neglected pathologies into rigorous engineering challenges, selecting biomaterials, designing cell signal processing assays, or diagnosing operational flaws in experimental platforms (e.g., PDMS compound absorption or Matrigel lot variability).
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。
Full-stack autonomous math modeling agent for CUMCM (全国大学生数学建模竞赛). Reads the problem statement and data, iterates through research → coding → verification → LaTeX writing, and produces a publication-quality PDF paper. Use when the user provides a CUMCM problem and wants end-to-end automated modeling, coding, and paper 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。
Full-stack autonomous math modeling agent for MCM/ICM (美国大学生数学建模竞赛). Handles team control number, problem choice (A–F), English academic writing, mcmthesis LaTeX template, and optional practical deliverable (memo/letter/report). Use when the user provides an MCM/ICM problem and wants end-to-end automated modeling, coding, and paper generation in English.
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 financial projections, P&L statements, DCF models, and valuation analyses from assumptions and historical data. Use when the user requests financial modeling or provides relevant inputs for this workflow.
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。
Workflow for predictive modeling, biomarker discovery, survival modeling, and explainability over omics-derived features.
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。
Database design specialist for schema modeling, query optimization, indexing strategies, and data integrityUse when "database design, schema, indexes, query optimization, migrations, normalization, database scaling, foreign keys, data modeling, database, sql, postgres, mysql, mongodb, schema, indexes, migrations, normalization, optimization" mentioned.
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Design and implement agent-based models (ABM) for simulating complex systems with emergent behavior from individual agent interactions. Use when "agent-based, multi-agent, emergent behavior, swarm simulation, social simulation, crowd modeling, population dynamics, individual-based, " mentioned.
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。