「sem」の検索結果

Performs molecular similarity searching using Tanimoto, Tversky, Dice, and cosine coefficients on bit/count fingerprints with explicit choice rules for symmetric vs asymmetric measures, scaffold-hopping vs lead-optimization regimes, activity-cliff diagnosis, and large-library nearest-neighbor methods (BulkTanimoto, Annoy MHFP6, USRCAT). Use when ranking compounds by structural resemblance to a query, clustering libraries, finding analogs, or diagnosing activity cliffs.

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Calculates molecular fingerprints (ECFP/Morgan, FCFP, MACCS, RDKit, AtomPair, TopologicalTorsion, Avalon, MAP4, MHFP6) and physicochemical descriptors (Lipinski, QED, TPSA, Crippen LogP, 3D shape) with explicit choice tables, bit vs count semantics, and partial-charge model selection. Use when featurizing molecules for similarity, QSAR, virtual screening, or ML, or selecting the correct fingerprint for a chemotype-aware task.

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 3D shape-based similarity searching using ROCS (OpenEye), USRCAT (ultra-fast), Open3DAlign (RDKit), ESPSim (electrostatic), and ShaEP with explicit handling of Tanimoto-Combo (shape + color), shape vs ECFP4 complementarity, conformer-ensemble searching, alignment optimization, and scaffold hopping. Use when searching for shape-mimicking compounds with different scaffolds, identifying bioisosteric replacements, prospective scaffold hopping, or expanding hit series beyond 2D similarity.

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 QSAR / QSPR models using chemprop D-MPNN, MolFormer, Uni-Mol, ChemBERTa, random forest baselines, and Gaussian processes with explicit handling of OECD 5 principles, applicability domain (kNN, leverage, conformal prediction, Mahalanobis), scaffold-balanced splits, ensemble uncertainty, calibration (Platt, isotonic), feature importance (SHAP, atomic attribution), and prospective validation. Use when building target-specific predictive models from in-house bioassay data, ADMET endpoints, or selectivity profiles.

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。

Comprehensive patterns and techniques for analyzing compiled binaries, understanding assembly code, and reconstructing program logic.

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。

Azure AI Search SDK for Python. Use for vector search, hybrid search, semantic ranking, indexing, and skillsets.

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。

Azure AI Search SDK for .NET (Azure.Search.Documents). Use for building search applications with full-text, vector, semantic, and hybrid search.

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 search applications with vector, hybrid, and semantic search capabilities.

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。

Inspect, rebuild, or verify the append-only SDD Composy semantic trace (events.jsonl) and its derived projection (trace.json). Use for 'o que aconteceu nessa task', 'histórico do fluxo', 'verificar o trace', 'rebuild the trace projection'. Do NOT use for a status overview (sdd-status), evidence manifests (sdd-evidence), or to edit trace history.

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。

Compõe a peça final no Figma sobre os ativos gerados — texto, marca, layout e grid. Camada OPCIONAL: use quando houver Figma conectado e o time precisar do arquivo editável. Dispara em "montar no Figma", "compor a peça", "colocar o texto na imagem", "aplicar a marca", "arquivo editável". Sem Figma, entrega especificação de composição.

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。

Discover, build, verify, and optionally export (HTML, optional PDF) the evidence manifest of one SDD Composy task in evidence_required, routing it toward review_required. Use when acceptance evidence must be assembled or checked — 'juntar as evidências da task', 'gerar o relatório de evidências', 'verify the evidence manifest'. Do NOT use to run tests (sdd-qa), review code (sdd-review), or inspect the trace (sdd-trace).

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。

Design traceable accessibility checks that combine scanners with observable keyboard, focus, semantic, and assistive-technology behavior.

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。

Enable, record, query, summarize, or verify the opt-in PWDEV Flow semantic audit trail. Use when the user asks what Flow actions occurred, wants audit integrity checked, or needs a portable JSONL record of workflow gates and artifacts.

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。

Interpreta métricas de copy e aponta o que fazer — conversão de landing, abertura e clique de e-mail, CTR e CPA de anúncio, engajamento de social. Use quando o usuário disser "analisar desempenho", "como foi a campanha", "os números do mês", "essa página não converte", "taxa de abertura caiu", "relatório de performance", ou colar métricas pedindo leitura. Não recomenda mudanças — ver perf-optimize.

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。

Transforma análise de desempenho em plano de ação priorizado por tiers — ganhos rápidos, mudanças estruturais, experimentos e o que parar de fazer. Use quando o usuário disser "o que eu faço agora", "como melhorar", "plano de ação", "por onde começo", "otimizar campanha", ou logo após perf-analyzer e perf-patterns. Fecha o ciclo: escrever → medir → aprender → escrever melhor.

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。

Estrutura narrativa para copy — PAS, BAB, AIDA, PASTOR, Jornada do Herói e Golden Circle. Use quando o usuário disser "storytelling", "contar a história", "narrativa da marca", "o texto está sem fio condutor", "case de sucesso", ou quando a página precisar de narrativa em vez de argumento listado.

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。

Infraestrutura para IA — GPU, servidores MCP, gateway de LLM, pipeline de inferência, custo de token, cache semântico e observabilidade de modelo. Use quando o usuário disser "GPU", "MCP server", "LLM", "inferência", "modelo", "gateway de IA", "custo de token", "rate limit da API de IA", "self-host de modelo".

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。

Pesquisa de Voz do Cliente (VOC) antes de escrever qualquer copy — coleta a linguagem literal do público em avaliações, fóruns, redes sociais, tickets de suporte e transcrições de vendas. Use quando o usuário disser "pesquisa de público", "voz do cliente", "VOC", "o que os clientes falam", "pesquisar concorrente", "analisar reviews", "levantar objeções", "não sei o que escrever", ou quando pedir copy sem que exista seção 6 preenchida em .claude/pwdev-copy-context.md. Produz frases verbatim, não paráfrases.

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。

Cria e mantém o inventário da plataforma — contas AWS, clusters, ambientes, bancos, hosts e o mapeamento que distingue produção de staging. Use quando o usuário disser "configurar devops", "mapear a infra", "qual o ambiente", "cadastrar cluster", ou quando qualquer skill não encontrar .claude/pwdev-devops-context.md. Fundação: sem este arquivo, o plugin não sabe o que é produção — e trata tudo como 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。

Build accessible web applications following WCAG 2.1/2.2 guidelines with proper semantic HTML, ARIA attributes, keyboard navigation, screen reader support, and inclusive design. Use when implementing ARIA labels and roles, ensuring keyboard navigation, supporting screen readers, providing text alternatives for images, managing focus, creating accessible forms, building inclusive UI components, testing with accessibility tools, meeting WCAG compliance levels, or designing for users with disabilities.

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。

Roslyn-powered semantic query CLI for C# solutions. Use when you need to find references, callers, implementations, dependencies, members, or trace call chains in a C# codebase — replaces multi-round grep/read cycles with single precise queries.

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Rewrite AEM Edge Delivery Services page content for AI search discoverability (GEO — Generative Engine Optimization). Analyzes content structure, semantic clarity, and factual density, then produces optimized rewrites that rank in both traditional search and AI-powered search engines. Use when optimizing pages for search visibility, AI discoverability, or content clarity.

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。

Semantic Design System Skill for Google Stitch. Generates agent-friendly DESIGN.md files that enforce premium, anti-generic UI standards — strict typography, calibrated color, asymmetric layouts, perpetual micro-motion, and hardware-accelerated performance.

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Derive and implement first-principles audio-coupled haptic and kinetic UI feedback. Use when designing, reviewing, or coding haptics/audio for mobile apps, native iOS/Android, React Native/Expo, SwiftUI, games, detent pickers, toggles, sliders, page curls, scroll gestures, wallet/signature flows, Solana transactions, backend/realtime state changes, confirmations, settlements, and semantic UI events where feedback should follow signal math instead of ad hoc vibration calls.

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。