「model」の検索結果

Predicts ADMET properties using ADMETlab 3.0 (119 platform features, including 77 prediction models with modeled-endpoint uncertainty), ADMET-AI, DeepChem MolNet, and chemprop D-MPNN with explicit handling of OECD QSAR principles, applicability domain assessment, calibration, hERG/CYP/AMES endpoints, and PAINS / Lipinski / Ro5 / Veber / BBB druglikeness filters. Use when filtering compounds for drug-likeness, prioritizing leads by predicted safety, or building an in-house ADMET QSAR model.

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 a new dbt Silver model with SQL + YAML contract. Use when adding a new Silver layer model from a raw source table.

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。

Validate trained models and analytical claims against their intended decision, independent evidence, and human-reviewed ground truth. Use when reviewing model performance, analysis conclusions, launch claims, or evaluation reports; use failure diagnosis instead when the main task is locating a known defect.

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 or maintain a source-grounded kpopper knowledge model as a versioned plugin. Use when a reusable domain model should ship independently of any one project.

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 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.

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。

Find, build, evaluate, and improve systems using decision models. Use for bounded classification, routing, ranking, Choice/Score/Noul, decision-model composition, evaluation harnesses, prompt/program hill climbing, and Software 3.0 workflows across software, business, organizations, and life. Separate models, code, and human judgment. TypeSafe Jev is the default hosted exemplar. Not for straightforward arithmetic, prose rewriting, or provider setup alone.

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 constructing base / upside / downside scenarios — three-statement modeling, sensitivity analysis, optionality reasoning. Triggers on 'model the scenarios', 'what if growth halves'.

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。

Construa, depure e otimize aplicativos com Claude API / SDK Anthropic. Aplicativos construídos com esta skill devem incluir cache de prompt. Também lida com migração de código Claude API existente entre versões de modelo (4.5 → 4.6, 4.6 → 4.7, substituições de modelo descontinuado). TRIGGER quando: código importa `anthropic`/`@anthropic-ai/sdk`; usuário solicita Claude API, SDK Anthropic ou Managed Agents; usuário adiciona/modifica/afina um recurso Claude (cache, thinking, compaction, tool use, batch, files, citations, memory) ou modelo (Opus/Sonnet/Haiku) em um arquivo; dúvidas sobre prompt caching / cache hit rate em um projeto SDK Anthropic. SKIP: arquivo importa `openai`/SDK de outro provedor, nome de arquivo como `*-openai.py`/`*-generic.py`, código agnóstico de provedor, programação geral/ML.

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。

Quando o usuário quer aplicar princípios psicológicos, modelos mentais ou ciências comportamentais ao marketing. Também use quando o usuário mencionar 'psicologia', 'modelos mentais', 'viés cognitivo', 'persuasão', 'ciência comportamental', 'por que as pessoas compram', 'tomada de decisão' ou 'comportamento do consumidor'. Esta skill fornece 70+ modelos mentais organizados para aplicação em marketing.

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。

Framework para embeddings de última geração em sentenças, textos e imagens. Oferece 5000+ modelos pré-treinados para similaridade semântica, clustering e retrieval. Suporta modelos multilíngues, específicos de domínio e multimodais. Use para gerar embeddings para RAG, busca semântica ou tarefas de similaridade. Melhor para geração de embeddings em produção.

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。

Garanta estrutura válida de JSON/XML/código durante a geração, use modelos Pydantic para outputs type-safe, suporte modelos locais (Transformers, vLLM) e maximize velocidade de inferência com Outlines - biblioteca de geração estruturada da dottxt.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。

Visualize métricas de treinamento, depure modelos com histogramas, compare experimentos, visualize grafos de modelos e perfil de desempenho com TensorBoard - kit de visualização de ML do Google

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。

Treina modelos de linguagem grandes (2B-462B de parâmetros) usando NVIDIA Megatron-Core com estratégias avançadas de paralelismo. Use ao treinar modelos >1B de parâmetros, precisar de máxima eficiência de GPU (47% MFU em H100), ou necessitar de paralelismo tensor/pipeline/sequência/contexto/expert. Framework pronto para produção usado em Nemotron, LLaMA, DeepSeek.

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。

Acelere a inferência de LLMs usando especulative decoding, múltiplas cabeças Medusa e técnicas de lookahead decoding. Use ao otimizar velocidade de inferência (aceleração de 1,5-3,6×), reduzir latência em aplicações em tempo real ou fazer deploy de modelos com recursos computacionais limitados. Cobre modelos draft, atenção em árvore, iteração de Jacobi, geração paralela de tokens e estratégias de deploy em produção.

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。

Avalia LLMs em mais de 60 benchmarks acadêmicos (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use quando benchmarking de qualidade de modelo, comparação de modelos, relatório de resultados acadêmicos ou rastreamento de progresso de treinamento. Padrão da indústria usado por EleutherAI, HuggingFace e laboratórios principais. Suporta HuggingFace, vLLM, APIs.

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。

Compare model candidates using weighted metrics and deterministic ranking outputs. Use for benchmark leaderboards and model promotion 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。

Structured Domain Modeling Architecture (SDMA) principles. Trigger when: - Designing domain models, building ologs (ontological graphs), verifying protocol equivalence, selecting schemas, or measuring architectural entropy. - Modeling structures using Category Theory, Coalgebra, or Information Theory. - Prompt contains keywords: sdma, olog, Category Theory, Coalgebra, Information Theory, entropy, schema, pullback, functor, monad.

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, extend, or fix Eloquent business models and their state transitions. Use when moving business rules out of controllers or Actions, or explicitly migrating a model into a DDD capability.

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。

Configure Azure API Management as an AI Gateway for AI models, MCP tools, and agents. WHEN: semantic caching, token limit, content safety, load balancing, AI model governance, MCP rate limiting, jailbreak detection, add Azure OpenAI backend, add AI Foundry model, test AI gateway, LLM policies, co...

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。

Conduct a defensible cybersecurity risk assessment using the NIST SP 800-30 Rev 1 methodology: prepare scope and a risk model, identify threat sources and threat events, identify vulnerabilities and predisposing conditions, determine likelihood and impact, compute risk, and communicate results as a prioritized risk register. Use when an organization needs an actual risk *assessment* (not a maturity score), when a control framework (CSF, ISO 27001, RMF, SOC 2, PCI) requires a documented risk analysis as input, when leadership asks "what are our top risks and how bad are they", when assessing risk for a new system or major change, or when building a risk register from scratch. This is the methodology that feeds framework selection, ATO packages, and treatment decisions. Keywords: risk assessment, NIST 800-30, threat modeling, likelihood and impact, risk register, risk analysis, threat sources, vulnerabilities, risk determination, qualitative risk, risk matrix, residual risk, risk treatment.

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。

Implement Microsoft's Enhanced Security Admin Environment (ESAE) tiered administration model for Active Directory. Covers Tier 0/1/2 separation, privileged access workstations (PAWs), administrative f

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。

Econometrics skill for panel data models. Activates when the user asks about: "panel data", "fixed effects", "random effects", "Hausman test", "within estimator", "between estimator", "two-way fixed effects", "clustered standard errors panel", "FE model", "RE model", "pooled OLS", "unobserved heterogeneity", "panel regression", "first difference estimator", "entity fixed effects", "time fixed effects", "面板数据", "固定效应", "随机效应", "豪斯曼检验", "双向固定效应", "面板回归", "个体效应", "时间效应", "一阶差分"

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。

Knowledge base from the NASA Probabilistic Risk Assessment Procedures Guide (NASA/SP-2011-3421, 2nd ed.). Use for quantitative PRA of aerospace and safety-critical systems: the risk triplet and scenario logic stack (MLD, ESD, event trees, fault trees, minimal cut sets), Bayesian data collection and parameter estimation, aleatory/epistemic uncertainty modeling and common-cause failure (Alpha Factor, beta-factor, CCBE), human reliability analysis (THERP, CREAM, NARA, SPAR-H), context-based software risk (CSRM), physics-based and structural/phenomenological models (stress-strength, limit states, FORM/SORM, NASGRO, range safety), uncertainty propagation and importance measures (F-V, RAW, Birnbaum, DIM), and launch-abort modeling with worked PRA examples. This is the QUANTITATIVE engine beneath NASA's risk doctrine — for the qualitative RIDM/CRM decision framework use the nasa-risk pack. Thin on programme management, organisational risk governance, and non-aerospace regulatory contexts; aerospace-focused and anchored to the 2011 second edition.

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。

多模型编排与模型切换策略。基于评分的模型选择、跨 Agent 委派（Gemini、Codex、Ollama）、Advisor 配对、升级触发器、权限矩阵与成本效率优化。

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。