'ngc' 검색 결과

Go バックエンドの品質チェック（golangci-lint + Swagger 生成）。「lint」「golangci-lint」「静的解析」「swagger」「OpenAPI」「ドキュメント生成」などのキーワードで自動適用。

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Pinecone으로 내용의 의미가 비슷한 정보를 찾는 검색 기반을 구축합니다. 데이터 등록과 조건별 필터링, 키워드 검색과의 결합을 다룹니다.

• 문서를 근거로 답하는 AI 검색 기반 구축
• 의미 검색과 키워드 검색 결합
• 범주나 가격에 따른 검색 결과 필터링

Chroma로 의미가 비슷한 문서를 찾는 시스템을 구축합니다. 문서와 부가 정보를 저장·갱신하고 조건으로 검색을 좁히며 AI 앱을 시제품으로 만듭니다.

• 의미가 비슷한 문서를 검색할 때
• 검색 자료 기반 AI 답변을 시험할 때
• 문서 데이터를 로컬에 저장할 때

Build AI agents with LangGraph by connecting processing steps into a graph. Define shared state, conditional paths, tool calls, saved checkpoints, and human review points to create workflows that retain information as they run.

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• Building an agent that calls tools in a loop
• Sharing accumulated findings between multiple agents
• Routing requests to different processing paths

Implement, update, test, or document TypeScript provider packages, the adapters connecting supported services and frameworks. This skill focuses on work under ts/packages/providers and directs the agent to read the provider workflow before making changes.

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• Implement a TypeScript provider
• Modify a service or framework adapter
• Test provider-specific changes

Connect .NET MAUI screens to application data using XAML or C#. Covers compile-time binding checks, change notifications, value conversion, and fallback displays to help implement bindings and investigate screens that fail to update.

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• Fix screens that do not reflect data changes
• Add compile-time checks to XAML bindings
• Convert and format values for display

Guides CDF Auto-Labeling workflows for annotating data such as videos. Routes tasks to specialized references, covering shipped examples, stage selection, prompt and configuration changes, pipeline migration, container retrieval, and output validation.

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• Starting automatic labeling for warehouse-safety videos
• Adapting a shipped workflow to a new domain
• Writing annotation prompts and question banks

Check DOCA installation health, prepare build and runtime requirements, and investigate environment failures such as missing libraries or memory settings. Helps choose container learning or hardware deployment paths based on the available system.

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• Check whether a DOCA installation is healthy
• Investigate library detection and runtime environment failures
• Start learning through an NGC DOCA container without hardware

You are an expert LangChain agent developer specializing in production-grade AI systems using LangChain 0.1+ and LangGraph.

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Design LLM applications using the LangChain framework with agents, memory, and tool integration patterns. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows.

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INVOKE THIS SKILL when routing a LangGraph agent with a decision model (TypeSafe Jev, SemIf) instead of an LLM, or when auditing an existing agent for LLM calls that only produce a routing decision. Covers langchain-typesafe Noul/Choice/Score, reading answers correctly, threshold design, and LangSmith Gateway wiring.

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INVOKE THIS SKILL when you need human-in-the-loop approval, custom middleware, or structured output. Covers HumanInTheLoopMiddleware for human approval of dangerous tool calls, creating custom middleware with hooks, Command resume patterns, and structured output with Pydantic/Zod.

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INVOKE THIS SKILL when building ANY retrieval-augmented generation (RAG) system. Covers document loaders, RecursiveCharacterTextSplitter, embeddings (OpenAI), and vector stores (Chroma, FAISS, Pinecone).

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Design LLM applications using the LangChain framework with agents, memory, and tool integration patterns. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows.

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Provides unit test, integration test, and mock AI patterns for LangChain4j applications. Creates mock LLM responses, tests retrieval chains, validates RAG workflows, and implements Testcontainers-based integration tests for Java AI services. Use when unit testing AI services, integration testing LangChain4j components, mocking AI models, or testing LLM-based Java applications.

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Provides LangChain4j patterns for implementing MCP (Model Context Protocol) servers, creating Java AI tools, exposing tool calling capabilities, and integrating MCP clients with AI services. Use when building a Java MCP server, implementing tool calling in Java, connecting LangChain4j to external MCP servers, or securing tool exposure for agent workflows.

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Provides integration patterns for LangChain4j with Spring Boot. Configures AI model beans, sets up chat memory with Spring context, integrates RAG pipelines with Spring Data, and handles auto-configuration, dependency injection, and Spring ecosystem integration. Use when embedding LangChain4j into Spring Boot applications, building Java LLM applications with @Bean configuration, or setting up Spring AI patterns.

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Plan, implement, or review langchain architecture work in an existing codebase with compatibility, security, and verification controls. Use when the user explicitly requests langchain architecture work.

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Expert in LangGraph - the production-grade framework for building stateful, multi-actor AI applications. Covers graph construction, state management, cycles and branches, persistence with checkpointers, human-in-the-loop patterns, and the ReAct agent pattern. Used in production at LinkedIn, Uber, and 400+ companies. This is LangChain's recommended approach for building agents. Use when "langgraph, langchain agent, stateful agent, agent graph, react agent, agent workflow, multi-step agent, langgraph, langchain, agents, state-machine, workflow, graph, ai-agents, orchestration" mentioned.

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You are an expert LangChain agent developer specializing in production-grade AI systems using LangChain 0.1+ and LangGraph.

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Design LLM applications using the LangChain framework with agents, memory, and tool integration patterns. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows.

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Publish a released version of the Apache SkyWalking AI Sessionizer LangChain plugin, apache-skywalking-asz-langchain, to PyPI. Says what a release manager sets up before the first upload, downloads the voted source package, verifies it, builds the source distribution and the wheel from plugins/langchain inside it, checks and installs them, and uploads them with twine. Use after a version is published.

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Provides integration patterns for LangChain4j with Spring Boot. Configures AI model beans, sets up chat memory with Spring context, integrates RAG pipelines with Spring Data, and handles auto-configuration, dependency injection, and Spring ecosystem integration. Use when embedding LangChain4j into Spring Boot applications, building Java LLM applications with @Bean configuration, or setting up Spring AI patterns.

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Builds LLM applications with LangChain including chains, agents, memory, tools, and RAG pipelines. Use when users request "LangChain setup", "LLM chain", "AI workflow", "conversational AI", or "RAG pipeline".

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