「modeling」の検索結果

Build and sharpen a project's domain model. Use when discussing codebase terminology, writing or editing a CONTEXT.md, or recording or editing an ADR.

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。

CQRS/ESが集約の境界定義とモデリングに与える影響を解説する。CQRSを導入すると集約は コマンド実行に必要な最小限の状態のみ保持すればよくなり、読み取り責務はリードモデルに 委譲できる。大きすぎる集約の軽量化、集約境界の再定義、イベントによる状態管理を支援する。 集約設計、CQRS導入時のモデリング見直し、パフォーマンス問題の解決時に使用。 対象言語: 言語非依存。 トリガー：「CQRSで集約が変わる」「集約が大きすぎる」「集約にメッセージ1000件」 「集約の更新が重い」「CQRS導入で集約を見直す」「集約を軽量化したい」 「集約にクエリ用データが混ざっている」「集約の境界を再定義」 といったCQRS/モデリング関連リクエストで起動。

Analyze French rental property investments with comprehensive financial modeling. Use when analyzing French properties, calculating mortgage payments, ROI, cash flow projections, or break-even analysis. Includes French-specific costs like arrangement fees (1%), registration fees (1.5%), survey fees (€750), and mandatory life insurance (0.4% annually).

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。

Geospatial risk modeling including catastrophe models, exposure analysis, and underwriting. Use when assessing spatial risk, building catastrophe models, analyzing exposure/hazard/vulnerability, or computing portfolio risk metrics.

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。

Personal finance assistant for budgeting, savings goals, investment tracking, debt optimization, tax preparation, insurance review, net worth tracking, and financial scenario modeling. Supports multi-currency, bank statement import (CSV/MT940/OFX), and locale-based tax rules. Use for any personal finance question: budget planning, expense tracking, portfolio allocation, FIRE calculations, debt payoff strategies, mortgage comparisons, tax deductions, insurance coverage, retirement planning, and life events with financial impact such as marriage, buying a house, changing jobs, having a baby, or going freelance.

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 agricultural climate risk systems for weather impact modeling, crop insurance, drought/flood prediction, soil moisture, and carbon tracking. Use when: 'assess crop climate risk', 'evaluate weather yield models', 'review crop insurance integration', 'audit drought prediction', 'check carbon sequestration tracking', 'analyze farm adaptation planning', 'evaluate DSSAT or APSIM 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。

Computational thinking as a problem-solving discipline independent of programming languages. Covers the four pillars (decomposition, pattern recognition, abstraction, algorithm design), computational problem-solving methodology (understand, decompose, generalize, formalize, verify), abstraction levels (from hardware through user interface), modeling and simulation, automata and formal languages (DFA, NFA, regular expressions, context-free grammars, Turing machines), computational complexity classes (P, NP, NP-complete, undecidable), and constructionist pedagogy (learning by building, Logo, Scratch, physical computing). Use when approaching unfamiliar problems, teaching problem-solving strategies, analyzing problem complexity, or bridging between domain knowledge and computational solutions.

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。

Three-dimensional art and sculptural thinking for art education. Covers additive and subtractive sculptural processes, armature construction, modeling in clay, carving principles, casting and moldmaking, assemblage and found-object sculpture, installation art as expanded sculpture, and the conceptual transition from pictorial to spatial thinking. Use when working with three-dimensional media, analyzing sculptural form, understanding spatial composition, or investigating the relationship between sculpture and site.

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。

Digital art tools, techniques, and workflows for art education. Covers raster and vector workflows, digital painting, photo manipulation, generative and procedural art, 3D modeling and rendering, pixel art, the relationship between traditional skills and digital execution, and ethical considerations of AI-generated imagery. Use when working with digital tools, evaluating digital art, or bridging traditional art concepts into digital practice.

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。

Complex systems and emergent behavior — chaos theory, fractals, neural networks, cellular automata, and computability. Analyzes dynamical systems for chaos via Lyapunov exponents, classifies bifurcations, measures fractal dimension, and models emergent phenomena. Use when analyzing chaotic systems, computing Lyapunov exponents, classifying bifurcations, measuring fractal dimension, modeling neural networks, or studying emergent behavior from simple rules.

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。

Compressive Vasomotion Hypothesis (CVH) — vasomotion as a fast compression sweep that collapses ambivalent neural resonances (the Bayesian-blur problem) into a definite state. Use when modeling the ~100ms taṇhā 'grab', precision-weighting as compression forcefulness, or the generative collapse step of vasocomputational active inference.

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。

Quantum computing framework for building, simulating, optimizing, and executing quantum circuits. Use this skill when working with quantum algorithms, quantum circuit design, quantum simulation (noiseless or noisy), running on quantum hardware (Google, IonQ, AQT, Pasqal), circuit optimization and compilation, noise modeling and characterization, or quantum experiments and benchmarking (VQE, QAOA, QPE, randomized benchmarking).

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。

CatColab Stock-and-Flow Diagrams - epidemiological and ecological modeling with stocks (accumulations), flows (rates), and mass-action ODE semantics for SIR models and population dynamics.

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 metabolic modeling (COBRA). FBA, FVA, gene knockouts, flux sampling, SBML models, for systems biology and metabolic engineering analysis.

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。

CatColab Schemas - database schema modeling distinguishing entities (tables) from attributes (columns). Foundation for ACSets (Attributed C-Sets) and AlgebraicJulia data structures.

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。

CatColab Regulatory Networks - signed graphs for molecular biology modeling gene regulatory networks with positive (activating) and negative (inhibiting) edges.

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。

CatColab Petri Nets - concurrent system modeling via places (states), transitions (events), and token flow. Foundation for process algebra, workflow, and chemical reaction networks.

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。

Chemical reaction network modeling with Catalyst.jl. Autopoietic systems, unit-aware ODEs, and interacting subsystems.

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。

CatColab Ologs (Ontology Logs) - category-theoretic knowledge representation where objects are concepts and morphisms are functional relationships. Foundation for database schemas and conceptual modeling.

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。

CatColab Decapodes - Discrete Exterior Calculus for PDE modeling on meshes via Decapodes.jl integration. Model physics equations compositionally with automatic 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。

CatColab Causal Loop Diagrams - systems dynamics modeling with reinforcing (R) and balancing (B) feedback loops, delays, and Lotka-Volterra semantics for strategic analysis.

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。

ANIMA as limit construction over condensed skill applications. Formalizes prediction markets as belief ANIMAs, structure dishes as condensation media, and impact as equivalence class change. Use for understanding agency at maximum entropy, compositional world modeling, or applying Scholze-Clausen condensed mathematics to AI.

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。

Performance measurement, attribution modeling, and marketing ROI analysis. Use when setting up tracking, analyzing campaign performance, building attribution models, or creating marketing reports.

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。

Advises on setting up LLM-based text classification as a validated measurement design. Covers codebook construction, learning-regime and model choice, prompt construction, validation against human coding with kappa and F1, hybrid human-LLM review, downstream measurement error, and reporting, plus a short resumable batch pattern with a rule-based baseline for repeated free text against a closed codebook. Use when the user wants to classify, code, or label text at scale. Category discovery goes to topic-modeling.

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。