「ngc」の検索結果

Idiomatic programming style, patterns, and conventions for Go (Golang) development. Trigger when: - Writing, refactoring, reviewing, or debugging Go code. - Files matching the pattern **/*.go (including go.mod, go.sum) are in the workspace or referenced. - Tasks involve: go fmt, go test, go build, go vet, golangci-lint. - Prompt contains keywords: go, golang, goroutine, channel, select, interface, err != nil, defer, panic, gofmt, struct.

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Azure AI Content Understanding SDK for Python. Use for multimodal content extraction from documents, images, audio, and video. Triggers: "azure-ai-contentunderstanding", "ContentUnderstandingClient", "multimodal analysis", "document extraction", "video analysis", "audio transcription".

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Route loader option, loaderDeps for cache keys, staleTime/gcTime/ defaultPreloadStaleTime SWR caching, pendingComponent/pendingMs/ pendingMinMs, errorComponent/onError/onCatch, beforeLoad, router context and createRootRouteWithContext DI pattern, router.invalidate, Await component, deferred data loading with unawaited promises.

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Manages local UI state and side effects in Flutter using the flutter_hooks package (v0.21.x) with HookWidget, useState, useEffect, useMemoized, useAnimationController, and custom hooks. Use this skill when replacing StatefulWidget boilerplate with hooks, reusing stateful UI logic across widgets, managing controller lifecycles (TextEditingController, AnimationController, ScrollController) without manual dispose, implementing countdown timers or subscriptions in widgets, mixing flutter_hooks with Riverpod via HookConsumerWidget, or any time the words 'flutter hooks', 'useEffect', 'useState', 'HookWidget', or 'flutter_hooks' appear in the request.

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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.

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LLMs, prompt engineering, RAG systems, LangChain, and AI application development

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Initialize, update, and visualize codebase or personal knowledge wikis using LangChain OpenWiki. Enforces OKF v0.2, Grounded Claims, interactive visualizer graph, and MCP page-job lifecycle.

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Bridges UIKit and SwiftUI by wrapping UIKit views and view controllers in SwiftUI with UIViewRepresentable and UIViewControllerRepresentable, embedding SwiftUI in UIKit with UIHostingController, and coordinating delegate callbacks. Use when integrating camera previews, map views, mail compose, document scanners, PDF renderers, text views with attributed text, or other UIKit-only or third-party UIKit SDK surfaces into a SwiftUI app, or when migrating a UIKit app to SwiftUI incrementally.

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Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production. Use when: langfuse, llm observability, llm tracing, prompt management, llm evaluation.

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Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration. TRIGGER — read BEFORE opening the target file; don't skip because it "looks like a one-liner" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens). SKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no provider named — don't Read the file).

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Sanguo RAG unresolved mention resolution loop. Use for: 三國 RAG, unresolved mentions, alias review, observed mentions, MCQ triage, manual roster seeds, false-positive exclusion, running run_resolution_loop.py, applying unresolved-triage answers, delegating 三國名詞查證 to a web-capable agent, or Pilot ETL chunk experiments with LangChain text splitters / LangGraph / LangFlow around 張飛長坂橋等小樣本流程。

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Debug LangChain and LangGraph agents by fetching execution traces from LangSmith Studio. Use when debugging agent behavior, investigating errors, analyzing tool calls, checking memory operations, or examining agent performance. Automatically fetches recent traces and analyzes execution patterns. Requires langsmith-fetch CLI installed.

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Debug LangChain and LangGraph agents by fetching execution traces from LangSmith Studio. Use when debugging agent behavior, investigating errors, analyzing tool calls, checking memory operations, or examining agent performance. Automatically fetches recent traces and analyzes execution patterns. Requires langsmith-fetch CLI installed.

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Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)

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把公众号文章、Markdown、笔记、文档或长文本拆成适合小红书/小绿书的完整发布包。先分析全文、论证关系和模块覆盖，输出含完整逐卡文案、布局 ID、3～5 个标题建议与发布正文的确认方案；确认后生成可逐元素编辑、移动、缩放、切换整套或单卡主题、编辑发布文案并导出 1080x1440 PNG 的独立 HTML。也适用于复用已有拆分方案或 v1/v2 卡片 JSON。

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 LangGraph-based agent with modern patterns

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Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production. Use when: langfuse, llm observability, llm tracing, prompt management, llm evaluation.

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Data structures and serialization formats for agent-to-agent communication. Covers message envelopes, structured output schemas, capability declarations, task handoff payloads, error/retry signaling, and context windows as data structures. Compares the version-pinned A2A, MCP, and JSON-RPC role boundaries covered by this bundle; OpenAI and LangChain-specific APIs require their own current sources. Teaches when to use rigid schemas versus free-form content with validation, typed versus untyped boundaries, and streaming versus batch. Activate on: "agent message format", "agent communication schema", "agent-to-agent protocol", "A2A protocol", "MCP message format", "structured output for agents", "agent interop", "interchange format", "agent serialization", "task handoff format", "capability declaration". NOT for: what agents say to each other (use agent-conversation-protocols), orchestration topology (use multi-agent-coordination), building agent infrastructure (use agentic-infrastructure-2026).

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。