「modeling」の検索結果

プロジェクトで使う業務用語や概念の境界を、具体例とコードの照合で明確にします。決まった定義を用語集に記録し、重要な設計判断の理由も必要に応じて残すスキルです。

• 曖昧な業務用語を整理したいとき
• GLOSSARY.mdの作成・更新
• 業務上の定義とコードの整合確認

一度の対話では収まらない大きな企画や変更を、決めるべき問いと依存関係に整理します。課題管理ツールに判断の記録を残し、実行に移るまでの道筋を明らかにするスキル。

• 複数の対話にまたがる大きな計画
• 未決事項と依存関係の整理
• 共同作業で判断の経緯を共有

計画や設計を掘り下げる質問を重ね、内容を具体化しながら設計判断の記録と用語集も作ります。検討と文書化を同じ対話の中で進めたいときに使うスキルです。

• 質問に答えながら計画を詰めたいとき
• 対話で設計を掘り下げたいとき
• 検討中に設計判断を記録したいとき

営業・提携・資金調達の候補者を調べて優先順位を付け、共通の知人を通じた紹介経路を探し、相手の近況と送り手の文体に合わせてメールやSNSの連絡文を下書きします。

• 特定業界の見込み客を探したいとき
• 提携先や投資家候補の優先順位付け
• 共通の知人からの紹介経路の調査

言語モデルに指示への応答や好ましい回答を学習させるため、TRLによる追加学習を案内するスキル。データ準備、学習方式の選択、報酬モデルの学習や評価を扱います。

• 回答例で指示に従うモデルを学習
• 回答の好みをDPOで学習したいとき
• 報酬モデルを使うRLHFの構築

Scaffold an aggregate root with entity, value objects, repository interface, domain events, and test stubs. Use when adding a new aggregate to an existing bounded context, modeling a new business concept that owns invariants, or generating the boilerplate for an entity + repo + events triplet.

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 provides an advanced financial modeling suite with DCF analysis, sensitivity testing, Monte Carlo simulations, and scenario planning for investment decisions

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。

Capability-based multi-tool matrix for 3D modeling, CAD, point clouds, rendering, GPU debugging, and fabrication. Covers mesh/parametric/photogrammetry and the path from idea to printed part or game-ready asset.

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。

Fits and diagnoses Python statistical models including OLS, GLM, discrete and mixed models, ARIMA and SARIMAX. Supports coefficient inference, marginal effects, model comparison and time series forecasting with explicit design and uncertainty checks. Used for econometrics and statistical modeling; for guided test selection with APA reporting, see statistical-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。

Fits probabilistic models for single-cell omics, including scVI batch integration, scANVI annotation, totalVI CITE-seq, MultiVI RNA/ATAC integration, and posterior differential expression. Use for generative modeling, reference mapping, multimodal analysis, or model-based uncertainty; use scanpy for standard preprocessing and exploratory 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。

Builds and checks Bayesian models with PyMC, including hierarchical models, NUTS MCMC, variational inference, mutable-data predictions, posterior predictive checks, diagnostics, and PSIS-LOO model comparison. Use for probabilistic modeling and uncertainty inference in PyMC.

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。

Simulates lithium-ion battery charge, discharge and rest experiments with PyBaMM, records parameter-set provenance, checks mesh and solver sensitivity, and compares predicted voltage curves with measured cycling data. Use for SPM or DFN electrochemical battery modeling, C-rate protocols, voltage cutoffs, parameter studies and numerical validation of battery simulations.

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。

Pharmacokinetic and pharmacodynamic modelling and simulation - non-compartmental analysis, compartmental and population PK, PK/PD and exposure-response, TMDD, PBPK orientation, bioequivalence, allometric scaling and first-in-human dose, drug interaction prediction, and Bayesian therapeutic drug monitoring. Use when analysing concentration-time data, deriving exposure metrics, fitting PK or PD models, or evaluating dosing regimens. Triggers include "pharmacokinetics", "pharmacodynamics", "PK/PD", "NCA", "non-compartmental", "AUC", "Cmax", "lambda z", "half-life", "clearance", "volume of distribution", "compartmental model", "population PK", "popPK", "NONMEM", "nlmixr2", "Pharmpy", "Monolix", "exposure-response", "Emax", "EC50", "indirect response", "effect compartment", "TMDD", "PBPK", "bioequivalence", "RSABE", "ABEL", "allometric scaling", "first-in-human", "MABEL", "drug-drug interaction", "DDI", "ICH M12", "concentration-QTc", "therapeutic drug monitoring", "MIPD", and "dosing regimen".

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。

Builds and validates PyHealth clinical machine-learning pipelines for EHR, signals, imaging, and medical codes. Use for PyHealth dataset loading, MIMIC-III/IV, eICU or OMOP prediction tasks, patient-level evaluation, mortality/readmission/length-of-stay modeling, medication recommendation, sleep staging, Trainer checkpoints, and ICD/ATC/NDC/RxNorm mapping.

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。

Discovers and evaluates scientific datasets, models, methodology posts, and Spaces through the Hugging Science catalog. Used when selecting scientific ML resources in biology, chemistry, genomics, materials, climate, physics, astronomy, medicine, mathematics, protein design, single-cell analysis, or PDE modeling, and when checking their actual datasets, Transformers, native-runtime, Inference Providers, or Gradio interfaces.

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。

Performs constraint-based metabolic modeling with COBRApy, including FBA, pFBA, FVA, gene knockouts, flux sampling, growth media, production envelopes, gap filling, and SBML model validation for systems biology and metabolic engineering.

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。

Google quantum computing framework. Use when targeting Google Quantum AI hardware, designing noise-aware circuits, or running quantum characterization experiments. Best for Google hardware, noise modeling, and low-level circuit design. For IBM hardware use qiskit; for quantum ML with autodiff use pennylane; for physics simulations use qutip.

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 STRIDE methodology to systematically identify threats. Use when analyzing system security, conducting threat modeling sessions, or creating security documentation.

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。

Step-by-step guide for capturing key application requirements for NoSQL use-case and produce Azure Cosmos DB Data NoSQL Model design using best practices and common patterns, artifacts_produced: "cosmosdb_requirements.md" file and "cosmosdb_data_model.md" file

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。

Enforce output quality, evidence verification, and quality gates across security audits. Use this skill when you perform SAST, SCA, threat modeling, or security code reviews. Use this skill to verify code locations and taint traces for all findings. Use this skill to prevent the suppression of valid security findings. Use this skill to evaluate reports against quality thresholds (score ≥ 8/10) before delivery.

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。

Real DCF (Discounted Cash Flow) model creation for equity valuation. Retrieves financial data from SEC filings and analyst reports, builds comprehensive cash flow projections with proper WACC calculations, performs sensitivity analysis, and outputs professional Excel models with executive summaries. Use when users need to value a company using DCF methodology, request intrinsic value analysis, or ask for detailed financial modeling with growth projections and terminal value calculations.

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。

Create institutional-quality equity research initiation reports through a 5-task workflow. Tasks must be executed individually with verified prerequisites - (1) company research, (2) financial modeling, (3) valuation analysis, (4) chart generation, (5) final report assembly. Each task produces specific deliverables (markdown docs, Excel models, charts, or DOCX reports). Tasks 3-5 have dependencies on earlier tasks.

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。

Advanced options strategies: volatility-surface modeling (SABR / Local Vol), dynamic Greeks rebalancing, calendar spreads, volatility arbitrage and skew trading, and option market-making basics.

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。

Quantitative statistical methods: ADF unit-root / cointegration tests, GARCH volatility modeling, regression diagnostics (heteroskedasticity / autocorrelation), Bootstrap, and hypothesis testing.

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。