1,022 件 ・ 関連度順
概要と使いどころ
应用从 AI for Everyone 蒸馏出的「AI转型五步指南 — 组织变革路线图」方法论。Use when the user needs practical guidance, diagnosis, workflow design, decision support, or an action plan related to ai-transformation-playbook.
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。
为 msModelSlim 创建基础 Transformers 模型适配器（Model Adapter）。 包含创建适配器、实现必需接口与注册安装流程。 适用：Decoder-only LLM、理解类 VLM（仅 LLM/text 部分）。 多模态生成（DiT/扩散，含双/多专家）见下方「多模态生成扩展」节，按需加载。 不适用：Encoder-only、非 Transformers 架构。
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。
Explains DSP math concepts to developers who need the theory behind an algorithm. Use whenever the user asks how a signal-processing concept works, wants intuition behind a formula, or hits a math-shaped bug. Trigger on phrases like "how does a Fourier transform work", "explain z-transforms", "what is convolution", "why does my biquad ring", "what is windowing for", "what causes aliasing", or "how do I convert gain to dB". Also trigger when the user pastes DSP code and asks why it behaves a certain way.
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 this skill when building, debugging, or optimizing CRM Analytics dataflows — defining node types (sfdcDigest, Append, Augment, computeExpression, computeRelative, Flatten, dim2mea, sfdcRegister), scheduling runs, handling run failures, and tuning performance. NOT for doing the same transformation in a Data Prep recipe — use admin/analytics-recipe-design. NOT for dataset field types, refresh schedules, or row limits — use admin/analytics-dataset-management.
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 this skill when designing or building CRM Analytics Data Prep recipes — including node selection, join patterns, bucket field configuration, formula expressions, and scheduling. Triggers: 'build a recipe', 'join datasets in analytics', 'bucket a measure field', 'schedule a recipe', 'data prep transformation'. NOT for dataflow JSON and its node types — use admin/analytics-dataflow-development. NOT for tuning a slow or oversized dataset — use data/analytics-dataset-optimization.
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。
Expert-level differential equations knowledge. Use when working with ordinary differential equations, partial differential equations, systems of ODEs, Laplace transforms, Fourier methods, boundary value problems, stability analysis, or numerical methods for ODEs. Also use when the user mentions 'ODE', 'PDE', 'initial value problem', 'boundary value problem', 'Laplace transform', 'separation of variables', 'eigenvalue problem', 'heat equation', 'wave equation', 'Laplace equation', 'stability', or 'phase plane'.
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。
Expert-level linear algebra knowledge. Use when working with vectors, matrices, determinants, eigenvalues, linear transformations, vector spaces, inner products, decompositions, or applications in machine learning and physics. Also use when the user mentions 'matrix', 'determinant', 'eigenvalue', 'eigenvector', 'linear transformation', 'vector space', 'basis', 'orthogonality', 'SVD', 'PCA', 'least squares', 'null space', or 'rank'.
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。
Expert-level special relativity knowledge. Use when working with Lorentz transformations, time dilation, length contraction, relativistic energy, mass-energy equivalence, spacetime, four-vectors, or relativistic mechanics. Also use when the user mentions 'Lorentz transformation', 'time dilation', 'length contraction', 'E=mc2', 'spacetime', 'four-vector', 'relativistic momentum', 'light cone', 'proper time', 'Minkowski', or 'relativistic velocity addition'.
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 manage trusted local Paseo plugins. Use when the user asks to create, edit, install, reload, enable, disable, remove, or troubleshoot a Paseo plugin; add lifecycle hooks; transform agent configuration, environment, MCP servers, or workspace creation; automate permissions or turn follow-ups; add a native surface, sidebar item, or workspace panel; add Command Center items or slash commands; add composer pills or attachment sources; transform, render, or append agent timeline items; contribute a theme; use Paseo from plugin code; or add plugin RPCs.
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。
Guides continuous improvement and change management for SMEs adopting AI. Applies Marginal Gains philosophy, Kaizen principles, and structured sprint methodologies to organizational transformation. Use when user asks about change management, continuous improvement, AI adoption strategy, team resistance, transformation roadmaps, or improvement sprints.
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 automaticamente changelogs voltados para usuários a partir de commits do git, analisando o histórico de commits, categorizando mudanças e transformando commits técnicos em notas de lançamento claras e amigáveis para clientes. Transforma horas de escrita manual de changelog em minutos de geração automatizada.
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。
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。
Modelo de espaço de estados com complexidade O(n) versus O(n²) dos Transformers. Inferência 5× mais rápida, sequências de milhão de tokens, sem cache KV. SSM seletivo com design aware de hardware. Mamba-1 (d_state=16) e Mamba-2 (d_state=128, multi-head). Modelos 130M-2.8B no HuggingFace.
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。
Implementação educacional do GPT em ~300 linhas. Reproduz o GPT-2 (124M) em OpenWebText. Código limpo e hackeável para aprender transformers. Por Andrej Karpathy. Perfeito para entender a arquitetura do GPT do zero. Treine em Shakespeare (CPU) ou OpenWebText (multi-GPU).
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。
Híbrido RNN+Transformer com inferência O(n). Tempo linear, contexto infinito, sem cache KV. Treina como GPT (paralelo), infere como RNN (sequencial). Projeto Linux Foundation AI. Produção em Windows, Office, NeMo. RWKV-7 (março de 2025). Modelos até 14B parâmetros.
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。
Unity Prefab / Scene / Asset の壊れた参照・Variant override・配線ミスを構造化応答で診断し、 dry-run → confirm 経路で安全に修復するための MCP ツールリファレンス。 Editor Bridge のセットアップ手順もここに含む。 トリガー: .prefab, .unity, .asset, .mat, Prefab, Variant, 配線, 参照, 壊れた参照, broken reference, GUID, fileID, override, パッチ, patch plan, validate refs, validate structure, where-used, inspect variant, inspect wiring, inspect hierarchy, wiring, null参照, null reference, MonoBehaviour, 階層, hierarchy, Transform整合, 孤立, orphan, 重複fileID, duplicate fileID, Editor Bridge, Bridge セットアップ, bridge setup, deploy_bridge, UNITYTOOL_BRIDGE_WATCH_DIR, watch ディレクトリ のいずれかに該当する作業で使用する。
Build transformer fine-tuning run plans with task settings, hyperparameters, and model-card outputs. Use for repeatable Hugging Face or PyTorch finetuning workflows.
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。
Deploy and use the Exasol Transformers Extension (TE) for NLP inference inside Exasol with notebook-connector. Covers `initialize_te_extension`, `deploy_scripts`, `PYTHON3_TE` language-container activation SQL, Hugging Face model-installation workflows, and the current TE SQL UDF surface.
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。
組織変革管理の専門コンサルタントスキル。システム導入、組織変革、DXプロジェクトにおける変革管理を支援。 ADKAR、Kotter 8-Stepフレームワークに基づき、ステークホルダー影響度分析、コミュニケーション計画、抵抗管理戦略を提供。 Use when managing organizational change, system implementations, digital transformation projects, or improving user adoption rates. Triggers: "change management", "organizational change", "system implementation", "user adoption", "change strategy", "stakeholder engagement".
Register a custom Listings callback for JetEngine — the per-field transform fired when a Dynamic Field widget renders meta values (Format date, Format number, Get post title, Convert units, …). Two registration paths are supported. Legacy 3-filter via jet-engine/listings/allowed-callbacks (label), jet-engine/listings/allowed-callbacks-args (control schema), and jet-engine/listing/dynamic-field/callback-args (positional args at runtime). Modern single-call $manager->register_callback($name, $label, $args) hooked on jet-engine/callbacks/register fires the three filters internally for you. Critical contract — the callback identifier you register MUST be a real PHP callable string (a global function name OR `Fully\\Qualified\\Class::method`). Bare static method names like `'unit_converter'` fail JE's is_callable() gate at apply_callback() and the rendered field silently becomes empty. Use when scaffolding a JetEngine companion plugin's listing-field transform, when seeing "callback appl...
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。
Write Estuary derivations in Python — async-generator transforms, Pydantic-typed source/output, optional persistent state, and dependency installation via uv. Use for ML/embeddings, async API calls, or any transform where SQL and TypeScript are awkward. Derivations add complexity and cost (and Python derivations require a private/BYOC data plane) — confirm the user wants a Python derivation before reaching for this. Use when user says "python derivation", "write a derivation in python", "embed with a python derivation", "ML derivation", "call an API from a derivation", or anything mentioning model2vec / huggingface / pydantic in a derivation context.
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。
Execute database migrations across ORMs and platforms with zero-downtime strategies, data transformation, and rollback procedures. Use when migrating databases, changing schemas, performing data transformations, or implementing zero-downtime deployment strategies.
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。
Train and fine-tune transformer language models using TRL (Transformers Reinforcement Learning). Supports SFT, DPO, GRPO, KTO, RLOO and Reward Model training via CLI commands.
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。