“modeling”的搜索结果

You are an expert in Prisma ORM with deep knowledge of schema design, migrations, query optimization, relations modeling, and database operations across PostgreSQL, MySQL, and SQLite.

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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.

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DDDに基づいたドメインモデルを設計します。戦略的設計（境界コンテキスト、ユビキタス言語）と戦術的設計（エンティティ、値オブジェクト、集約、ドメインサービス、ドメインイベント）を定義し、ヘキサゴナルアーキテクチャとの統合を行います。ScalarDBのトランザクション境界や管理テーブル分類の考慮事項を含みます。中間状態はresearch/に記録されます。

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Use this skill when designing MongoDB schemas, defining Mongoose models, choosing embed-vs-reference, or adding indexes. Trigger words: スキーマ設計, モデル定義, Mongoose, コレクション, インデックス, 埋め込み, 参照, embed, reference, index.

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

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Prisma ORM expert for schema design, migrations, query optimization, relations modeling, and database operations. Use PROACTIVELY for Prisma schema issues, migration problems, query performance, relation design, or database connection issues.

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Expert guidance for distributed NoSQL databases (Cassandra, DynamoDB). Focuses on mental models, query-first modeling, single-table design, and avoiding hot partitions in high-scale systems.

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This skill should be used when the user asks to "model agent mental states", "implement BDI architecture", "create belief-desire-intention models", "transform RDF to beliefs", "build cognitive agent", or mentions BDI ontology, mental state modeling, rational agency, or neuro-symbolic AI integration.

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Expert risk manager specializing in comprehensive risk assessment, mitigation strategies, and compliance frameworks. Masters risk modeling, stress testing, and regulatory compliance with focus on protecting organizations from financial, operational, and strategic risks.

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Design author-friendly content structures that work for non-technical content creators. Use when designing content structures for blocks in AEM Edge Delivery Services.

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Create, edit, or debug parametric 3D CAD parts in CadQuery (Python) that export to STEP for SolidWorks. Use when the request involves CadQuery, .step/.stl export, or modelling a physical part — enclosure, bracket, PCB, motor mount, cam, gear. Also use when a STEP will not open in CAD, or geometry looks correct in STL but is invalid as a solid. Applies a verification discipline before export and documented OCC pitfalls.

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Execute a single deterministic Python script to compute WACC, DCF assumptions, projected free cash flows, enterprise value, and intrinsic value per share. Updates the metadata markdown.

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Assess a company's economic moat, EBITA margin trajectory, and organic growth trajectory from analyst reports, earnings transcripts, or long-form articles. Produces structured qualitative outlook that feeds into the Financial Modeling skill's assumption generation.

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当使用者说“按我的模型写代码”“把方程实现成 Python/MATLAB/R”“调试这段建模代码”“补验证和灵敏度分析”“让结果可复现”时使用。只实现学生已确认的模型与数据契约，先写实现合同、手算小例、不变量和验收矩阵，再按读取、变换、求解、验证、敏感性与导出拆分；执行后记录输入快照、种子、环境、求解设置和输出，不静默修数据、不硬编码结果、不替学生发明模型或伪造数据。

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当使用者贴出数学建模题、数学建模竞赛题、研究问题或评分要求，说“帮我拆题”“这道题到底要什么”“有哪些隐含条件”“先别急着选模型”时使用。把原题整理成可核对的题面契约、变量与单位账本、子问题依赖图、歧义清单和下一步交接；区分原题事实、合理推断与待确认项，不选模型、不解题、不补造数据。

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当需求还只是"帮我写个东西""让 AI 做这个任务"这种一句话时使用。用至多 3–5 个高杠杆追问把它补成一份可交付的任务简报——目标、产物、受众、已有素材、约束、验收标准、不做什么、待确认假设，外加一段可直接粘贴的执行 prompt。简报的九个标题保持英文（内容用什么语言都行），字段完整性由 scripts/check_brief.py 校验。产出不绑定任何一家模型，人或 Agent 都能照着执行。不适用于：写歌交给 lyric-concept-builder，复习计划交给 deadline-to-study-plan，建模题交给 modeling-problem-reading-coach，物理题交给 physics-problem-router；使用者坚持直接要成品、一个问题都不肯答时也不用本 skill。

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当使用者说“帮我写模型假设”“这些假设合理吗”“方程里藏了哪些前提”“这些模型假设怎么检验”时使用。把题面事实、定义、校准选择、数值近似与真正假设分开，建立带类型、理由、作用域、失效后果、承重性和反证测试的假设登记表，并检查量纲、守恒、边界、极限情形与重复假设；不虚构现实依据，不把方便计算写成已验证事实。

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Build and sharpen a project's domain model. Use when the user wants to pin down domain terminology or a ubiquitous language, record an architectural decision, or when another skill needs to maintain the domain model.

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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.

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Performs logistic regression for clinical trial outcomes (binary, ordinal, multinomial) with marginal-vs-conditional estimand reporting per FDA 2023 covariate adjustment guidance, g-computation/standardisation for marginal effects, modified Poisson for RR, Brant test for proportional odds, Firth penalty for separation, and Hauck-Donner detection. Use when modeling binary or ordinal endpoints in confirmatory or exploratory clinical trials.

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Cost modeling and ROI analysis for specialized LLM development. Use when deciding whether to train a custom model, estimating total cost, or calculating break-even vs frontier APIs. Covers training costs, inference costs, and time-to-ROI projections.

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Expert guidance for GRPO/RL fine-tuning with TRL for reasoning and task-specific model training

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End-to-end bulk time-course analysis from an expression matrix to temporal gene modules and per-cluster pathway enrichment. Orchestrates temporal DE (limma splines or DESeq2 LRT), Mfuzz/tslearn soft clustering of expression-profile shapes, GAM trajectory fitting, per-cluster GO enrichment against a temporal-gene background, and an OPTIONAL circadian rhythm-detection branch (MetaCycle/CosinorPy) that runs only when the design covers >=2 full cycles with >=6-8 evenly spaced samples per cycle. Use when analyzing a bulk time-series expression experiment from any omics platform and deciding limma-splines vs DESeq2-LRT for temporal DE, soft vs hard clustering, whether the sampling design even licenses rhythm detection, and which background to use for enrichment. Not for single-cell pseudotime (see temporal-genomics/trajectory-modeling for the bulk-vs-pseudotime boundary) or unknown-period discovery (see temporal-genomics/periodicity-detection).

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End-to-end post-GWAS causal inference pipeline orchestrating heritability partitioning, genetic correlation, Mendelian randomization with CHP-aware sensitivity (CAUSE / LHC-MR), colocalization, fine-mapping with SuSiE / FOCUS, mediation, TWAS triangulation, cis-pQTL drug-target MR, effector-gene prioritization (L2G / PoPS / cS2G), and GenomicSEM common-factor GWAS. Use when triangulating causal inference across multiple complementary methods, prioritizing tissues via stratified LDSC, nominating or de-risking drug targets, mapping a lead SNP to a candidate effector gene, modeling shared genetic architecture across correlated traits, or producing a STROBE-MR-compliant publication-grade evidence battery from GWAS summary statistics.

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