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Author or upgrade Model Advisor checks for Simulink and System Composer models. Covers the full lifecycle: guideline authoring, check specification, Model Advisor check implementation, and qualification testing. Use when creating new checks (edit-time, standard batch, config-parameter, auto-fix), converting legacy StyleOne/StyleTwo/StyleThree checks to modern DetailStyle, creating a guideline for modeling, enforcing a modeling rule, or testing/qualifying a check. Triggers on any modeling constraint (e.g., "blocks shall...", "signals must match...", "parameters shall be set to...").

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。

Generate C/C++ or CUDA code from an AI model (PyTorch, LiteRT) using MATLAB Coder or GPU Coder. Use when the user wants to integrate an AI model into an application with code generation as the end goal — generating MEX, CUDA MEX, static library, dynamic library, or executable — or using the model in Simulink for simulation and code generation. Covers PyTorch ExportedProgram (.pt2) via loadPyTorchExportedProgram and LiteRT (.tflite) via loadLiteRTModel (R2026a+). Keywords: PyTorch, torch, .pt2, ExportedProgram, loadPyTorchExportedProgram, invoke, codegen, MEX, CUDA, GPU, C, C++, deploy, AI model, deep learning model, LiteRT, TFLite, TensorFlow Lite, Simulink, slbuild, PyTorch ExportedProgram block, MATLAB Function block, dlosslib, loadLiteRTModel.

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 for WeChat Mini Program AI via wx.cloud.extend.AI (小程序, wx.cloud apps). Covers generateText and streamText with callbacks (onText, onEvent, onFinish); streamText needs a data wrapper, generateText returns the raw response. Models via wx.cloud.extend.AI.createModel with groups hunyuan-exp (小程序成长计划), cloudbase (main managed), or custom-*; model id goes in the data wrapper `model` field. MUST run two-step preflight before code — see body. NOT for browser/Web (use ai-model-web), Node.js backend (use ai-model-nodejs), or image generation (use ai-model-nodejs).

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 a browser/Web app (React, Vue, Next, Nuxt, static sites, SPAs, dashboards, AI chat UI, 页面, 前端, 网页) needs AI models via @cloudbase/js-sdk. Default routing for Web/frontend AI — call directly from the browser, do NOT propose a Node.js proxy. Covers generateText and streamText; models via ai.createModel with groups cloudbase, hunyuan-exp, or custom-*, model id in the `model` field. MUST run two-step preflight before code — see body. NOT for Node.js backend (use ai-model-nodejs), Mini Program (use ai-model-wechat), or image generation (Node SDK only).

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 should be used whenever the user mentions a "semantic model", "data model", or "dataset", or asks to "build", "model", "design", "optimize", "review", or "audit" one, or to "add a measure", "add a relationship", "create a role" / "set up RLS", "add a calculation group", "set up incremental refresh", "fix a star schema", "reduce model size", "prepare a model for Copilot / AI", or "check model quality". Covers the full lifecycle (design, build, refresh, review) and drives every operation through the `te` CLI first, then TOM (connect-pbid) or a model MCP, then TMDL authoring (the tmdl skill). Not for report visuals (use pbir-cli) or isolated DAX query tuning (use the dax skill).

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。

Search all Japanese NLP resources (libraries, models, datasets, tutorials, dictionaries, Hugging Face). Accepts keywords or natural language questions in any language. Use whenever the user asks which Japanese NLP resource to use, or wants to find one: tokenizers / morphological analyzers, BERT or LLM models, embeddings, NER, text classification, datasets / corpora, dictionaries, tutorials, or Hugging Face models. Trigger phrases include '日本語の形態素解析ライブラリ', 'おすすめの日本語tokenizer', '日本語BERTモデル', '日本語の感情分析データセット', '日本語LLM 一覧', 'which Japanese embedding model', 'Japanese NER library'.

Lifecycle SOP for **per-model prompt artifacts** — the compiled prompts, instructions, few-shot demos, edit-format pins, and embedding-bound indices that change behavior when the underlying LM, dataset, or framework version changes. Activate when adopting compiled prompts (DSPy, GEPA, BootstrapFewShot output), when supporting multiple LMs in production, when a provider deprecates a model snapshot, or when a framework deprecates a config surface (LlamaIndex `ServiceContext` → `Settings`, Aider edit-format defaults). Do NOT activate for one-off raw prompt edits or for truly model-agnostic system prompts that have been swap-tested. Search keywords: prompt portability, model swap, recompile prompt, model deprecation, prompt per model, prompt breaks on new model, version compiled prompts.

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。

Split a multi-call LM workflow by cognitive load, not by accuracy: let one strong model make the few reasoning decisions and a cheap model do the many mechanical executions (Aider architect+editor, DSPy optimizer-LM vs task-LM, vLLM speculative draft+target, LangGraph supervisor+worker are the same shape). Use when designing or cost-optimizing a pipeline that calls an LM many times, when deciding which steps need a strong reasoner vs a cheap executor, or when adding an escalation valve for when the cheap tier degrades. Search keywords: reduce LLM cost, cheaper model, lower token cost, model cascade, route to cheap model, strong model plus cheap model, LLM cost 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。

Generate a coherent, whole-paper mathematical modeling solution framework from a complete contest problem. Use after the user supplies a CUMCM or other modeling problem and asks for modeling ideas, an overall solution plan, question-by-question analysis, candidate-model comparison, model-selection reasons, innovations, validation, or cross-question linkage. For every question, explain the task and mathematical essence, compare multiple feasible models in tables, recommend a route with explicit reasons, and keep all questions connected through shared data, variables, parameters, constraints, and validation.

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。

CesiumJS models, glTF, and particle effects - Model, KHR_meshopt_compression, CAD glTF extensions, EdgeDisplayMode, ModelAnimation, ModelNode, ParticleSystem, emitters, GPM extensions. Use when loading compressed or CAD-style glTF/GLB models, controlling edge rendering, playing model animations, positioning particles, or working with geospatial positioning metadata.

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。

Register trained models in MLflow Model Registry with version control, implement stage transitions (Staging, Production, Archived) with approval workflows, and manage model lineage with comprehensive metadata and deployment tracking. Use when promoting a trained model from experimentation to production, managing multiple model versions across development stages, implementing approval workflows for governance, rolling back to previous versions, or auditing model changes for compliance.

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。

Guide for choosing models in Tinker — available model families, model types (base, instruction, reasoning, hybrid, vision), architecture (dense vs MoE), and how to match renderers to models. Use when the user asks which model to use, what models are available, or how to pick a model for their 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。

Investigate model specifications, requirements, and evaluation strategy. Use when researching a model before benchmarking: reading HuggingFace model cards, estimating VRAM requirements, selecting GPU providers, and determining evaluation approach. Triggers on "model research", "investigate model", "model info", "VRAM estimate", "which provider", "model card".

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。

Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box 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。

Windows アプリ開発 (Windows AI Foundry) の AI/ML 統合リファレンス。 Windows AI APIs、LanguageModel, TextRecognizer, ImageScaler, ImageObjectExtractor, ImageObjectRemover, ImageDescriptionGenerator, SemanticSearch, ContentModeration, Phi Silica LoRA、Windows ML、ONNX Runtime、execution providers、LearningModel、 Foundry Local、CLI、REST API、SDK、model catalog、MCP サーバー、App Actions、 IActionProvider、Agent Launchers、DirectML、IDMLDevice、operators、tensors。

OpenClawにモデル提供サービスを追加し、実際の応答まで確認するスキル。認証情報は環境変数やファイルへの参照で扱い、設定の検証と結果報告も進めます。

• OpenClawにモデルを追加したいとき
• 認証情報を参照形式で設定したいとき
• モデルの実際の応答を確認したいとき

Add descriptions for new models from the HuggingFace router to chat-ui configuration. Use when new models are released on the router and need descriptions added to prod.yaml and dev.yaml. Triggers on requests like "add new model descriptions", "update models from router", "sync models", or when explicitly invoking /add-model-descriptions.

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。

Two related workflows for a locally-cloned codebase, in one skill. Documentation mode produces a single, comprehensive, verifiable architecture document primarily by reading files on disk (local-first) — use it whenever the user wants to understand, map, document, research, or onboard onto a codebase ("research this repo", "write up the architecture", "do an architecture deep dive", "document how this codebase works", "map the system design", "create an onboarding doc"). Modernization mode generates a phased plan to modernize, migrate, upgrade, or rewrite a legacy system ("modernize this", "plan the migration", "how would we rewrite this", "how do we get off this legacy stack"); if no architecture document exists yet it first runs Documentation mode, then continues straight through to the plan. It assumes the legacy stack may be dead, runs a time-boxed feasibility spike, and picks the highest achievable rung on a safety ladder instead of demanding a fully-green legacy CI gate up front.

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。

Audit a spreadsheet for formula accuracy, errors, and common mistakes. Scopes to a selected range, a single sheet, or the entire model (including financial-model integrity checks like BS balance, cash tie-out, and logic sanity). Triggers on "audit this sheet", "check my formulas", "find formula errors", "QA this spreadsheet", "sanity check this", "debug model", "model check", "model won't balance", "something's off in my model", "model review".

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。

Migrate a legacy-template SGLang cookbook page (monolithic per-model generator under docs/src/snippets/autoregressive/) onto the config-driven template (shared _deployment.jsx / _playground.jsx engines + per-model config). Use when asked to migrate, convert, or port an existing cookbook page — NOT for brand-new models (use cookbook-add-model for those). Run with /cookbook-migrate-model <Model page name, e.g. GLM-5.1>.

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。

Merge multiple fine-tuned models using mergekit to combine capabilities without retraining. Use when creating specialized models by blending domain-specific expertise (math + coding + chat), improving performance beyond single models, or experimenting rapidly with model variants. Covers SLERP, TIES-Merging, DARE, Task Arithmetic, linear merging, and production 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。

Maintain the supported LLM model list: add a new model, or run routine maintenance to verify availability and discover new models worth adding. Use when the user asks to add/support a model, update the model list, or check model availability.

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。

Agent Platform Model Registry Management. Use when you need to upload, list, describe, update, or delete machine learning models (and their versions) in the Agent Platform Model Registry. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform models.

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。

Connects to and performs inference with Google Cloud Agent Platform GenAI models, including First-Party Gemini models and Third-Party OpenMaaS models (Llama, DeepSeek, Qwen, etc.). Use when asked to perform inference, ask a model a question, run a test prompt, execute chat completions, or generate code for calling Gemini or OpenMaaS models, authenticate with GenAI SDK, OpenAI SDK, or legacy Agent Platform SDK, configure base URLs and global/regional endpoints, or troubleshoot 429 Resource Exhausted (DSQ), 400 User Validation, or 404 Not Found errors. Don't use for deploying models to endpoints or for running model evaluations.

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。