「ida」の検索結果

NVIDIA RAG Blueprint deployment, configuration, troubleshooting, and shutdown guidance for Docker, Helm, and library-based RAG stacks.

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。

Route Google Stitch tasks to the correct imported Stitch skill, with verified MCP tool boundaries, upload safety, and cross-client fallback guidance.

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。

Serena MCP for project memory and code navigation. Use when managing Serena memories, navigating symbols, performing intelligent refactoring, or maintaining context/continuity across AI agent sessions.

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。

MongoDB schema design patterns and anti-patterns. Use when designing data models, reviewing schemas, migrating from SQL, or troubleshooting performance issues caused by schema problems. Triggers on "design schema", "embed vs reference", "MongoDB data model", "schema review", "unbounded arrays", "one-to-many", "tree structure", "16MB limit", "schema validation", "JSON Schema", "time series", "schema migration", "polymorphic", "TTL", "data lifecycle", "archive", "index explosion", "unnecessary indexes", "approximation pattern", "document versioning".

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。

NVIDIA NeMo Retriever deployment and usage guidance for local retrieval services, corpus ingestion, and grounded question-answering 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。

JUnit 5 testing patterns and parameterized-test guidance. Use when writing or reviewing Java unit tests.

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。

MANDATORY prerequisite — load this skill BEFORE every `generate_diagram` tool call. NEVER call `generate_diagram` directly without loading this skill first. Trigger whenever the user asks to create, generate, draw, render, sketch, or build a diagram — flowchart, architecture diagram, sequence diagram, ERD or entity-relationship diagram, state diagram or state machine, gantt chart, or timeline. Also trigger when the user mentions Mermaid syntax or wants a system architecture, decision tree, dependency graph, API call flow, auth handshake, schema, or pipeline visualized in FigJam. Routes to type-specific guidance, sets universal Mermaid constraints, and tells you when to use a different diagram type or skip the tool entirely (mindmaps, pie charts, class diagrams, etc.).

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。

AI demos and GPU compute with Gradio Spaces and Hugging Face Spaces ZeroGPU. Use when writing or reviewing code that uses `@spaces.GPU`, configuring `python_version` or `requirements.txt` for a ZeroGPU Space, or handling ZeroGPU-specific code constraints — pickle-based process isolation, `gr.State` semantics across the worker boundary, no `torch.compile` (use AoTI instead), CUDA wheel-only builds (no `nvcc` at build or runtime), large vs xlarge sizing, and dynamic duration callables. Make sure to use this skill whenever the user mentions ZeroGPU, `@spaces.GPU`, or the `spaces` Python package, or hits ZeroGPU-specific code errors like `PicklingError` across the worker boundary, `illegal duration`, or `flash-attn` wheel-build failures — even when the user does not explicitly ask for ZeroGPU coding guidance. Trigger on `import spaces` or `@spaces.GPU` in code.

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 publish a Gradio demo on Hugging Face Spaces for a user-provided LoRA. Use when someone asks to create, generate, ship, or publish a Space, demo, Gradio app, or playground for a LoRA — including LoRAs for Qwen-Image, Qwen-Image-Edit, LTX-Video, Wan, FLUX, SDXL, or other diffusion base models. Also triggers when someone describes a LoRA they trained or hosts on the Hub and wants to share it. Covers picking the right base pipeline and `diffusers` inference recipe, designing a UI tailored to the LoRA's task and inputs (Union/multi-task control, edit, video, image, etc.), respecting model-card recommendations (trigger words, steps, guidance, LoRA scale, example inputs), and shipping to ZeroGPU hardware as a private Space by default.

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。

Scan a codebase for deepening opportunities, present them as a visual HTML report, then guide a user-directed decision loop for the selected candidate.

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 for explicit undergraduate data-science teaching, assignment guidance, concept clarification, method selection, or output interpretation. Do not activate merely because a task contains data or Python code.

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。

Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV/Parquet I/O, nullable semantics, and multi-GPU DataFrame workloads.

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。

NVIDIA DeepStream model-import guidance for bringing vision models from Hugging Face or NVIDIA NGC into DeepStream pipelines with export, TensorRT build, and benchmark steps.

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, validate, and invoke .agent.md custom agents. Use when finding agent files in the local Claude or VS Code Insiders directories, checking frontmatter, verifying disable-model-invocation, or determining agentName for runSubagent 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。

Validate at every layer data passes through to make bugs impossible

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Validate GCI competition predictions and notebook outputs against expected schema, metric, and submission constraints.

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。

NVIDIA DeepStream SDK development guidance for Python pyservicemaker pipelines, video analytics, TensorRT integration, and streaming inference 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。

xUnit testing patterns and data-driven test guidance. Use when writing or reviewing .NET unit tests.

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 modernizing and migrating MSBuild project files to SDK-style format. Only activate in MSBuild/.NET build context. USE FOR: converting legacy .csproj/.vbproj with verbose XML to SDK-style, migrating packages.config to PackageReference, removing Properties/AssemblyInfo.cs in favor of auto-generation, eliminating explicit <Compile Include> lists via implicit globbing, consolidating shared settings into Directory.Build.props. Indicators of legacy projects: ToolsVersion attribute, <Import Project="$(MSBuildToolsPath)">, .csproj files > 50 lines for simple projects. DO NOT USE FOR: projects already in SDK-style format, non-.NET build systems (npm, Maven, CMake), .NET Framework projects that cannot move to SDK-style. INVOKES: dotnet try-convert, upgrade-assistant tools.

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 organizing MSBuild infrastructure with Directory.Build.props, Directory.Build.targets, Directory.Packages.props, and Directory.Build.rsp. Only activate in MSBuild/.NET build context. USE FOR: structuring multi-project repos, centralizing build settings, implementing NuGet Central Package Management (CPM) with ManagePackageVersionsCentrally, consolidating duplicated properties across .csproj files, setting up multi-level Directory.Build hierarchy with GetPathOfFileAbove, understanding evaluation order (Directory.Build.props → SDK .props → .csproj → SDK .targets → Directory.Build.targets). Critical pitfall: $(TargetFramework) conditions in .props silently fail for single-targeting projects — must use .targets. DO NOT USE FOR: non-MSBuild build systems, migrating legacy projects to SDK-style (use msbuild-modernization), single-project solutions with no shared settings. INVOKES: no tools — pure knowledge 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。

Creates .NET projects from templates with validated parameters, smart defaults, Central Package Management adaptation, and latest NuGet version resolution. USE FOR: creating new dotnet projects, scaffolding solutions with multiple projects, installing or uninstalling template packages, creating projects that respect Directory.Packages.props (CPM), composing multi-project solutions (API + tests + library), getting latest NuGet package versions in newly created projects. DO NOT USE FOR: finding or comparing templates (use template-discovery), authoring custom templates (use template-authoring), modifying existing projects or adding NuGet packages to existing projects.

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 de produto nivel Apple — sistemas visuais, UX flows, acessibilidade, linguagem visual proprietaria, design tokens, prototipagem e handoff. Cobre Figma, design systems, tipografia, cor, espacamento, motion design e principios de design cognitivo.

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LLM Operations -- RAG, embeddings, vector databases, fine-tuning, prompt engineering avancado, custos de LLM, evals de qualidade e arquiteturas de IA para producao.

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 guidance for deep learning, transformers, diffusion models, and LLM development with PyTorch, Transformers, Diffusers, and Gradio.

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。