156 件 ・ 関連度順
概要と使いどころ
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.
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 SurrealDB 3 architect and developer skill (tracks v3.1.4+). SurrealQL mastery, multi-model data modeling (document, graph, vector, time-series, geospatial), schema design, security, deployment, performance tuning, SDK integration (JS, Python, Go, Rust, Java, .NET, C, PHP, Swift, Kotlin, Ruby), Surrealism WASM extensions, SurrealML scope coverage (preview), built-in MCP (v3.1+) and standalone SurrealMCP, LangChain Python integration, editor tooling (LSP v0.1.6, tree-sitter, CodeMirror v1.0.6, VS Code/JetBrains/Neovim/Zed), and ecosystem integrations (Surrealist v3.9, Surreal-Sync, SurrealFS, SurrealKit v0.7, n8n, Agent Skills, setup-surreal). Universal skill for 30+ AI agents.
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。
Production-grade Python and AI engineering standards. Use for ANY Python work — FastAPI services, LangChain/LangGraph agents, RAG pipelines, LLM API integrations, document processing, or data scripts — whether writing new code, reviewing, refactoring, or debugging. Enforces typed Pydantic boundaries, structured errors and logging, LLM call timeouts/retries/output validation, secret management, async correctness, and tested 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。
Imported skill server from langchain
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。
Multi-agent orchestration framework for autonomous AI collaboration. Use when building teams of specialized agents working together on complex tasks, when you need role-based agent collaboration with memory, or for production workflows requiring sequential/hierarchical execution. Built without LangChain dependencies for lean, fast execution.
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。
LLM application architecture expert for RAG, prompting, agents, and production AI systemsUse when "rag system, prompt engineering, llm application, ai agent, structured output, chain of thought, multi-agent, context window, hallucination, token optimization, llm, rag, prompting, agents, structured-output, anthropic, openai, langchain, ai-architecture" mentioned.
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 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, monitor llm, debug llm, langfuse, observability, tracing, llm-monitoring, evaluation, prompt-management, debugging, analytics" mentioned.
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。
E2B is a cloud platform that gives AI agents secure, isolated sandboxes — ephemeral Linux VMs with a filesystem, package managers, and internet access — to run AI-generated code safely. Use when a user wants an agent to execute Python or JavaScript, install packages, read/write files, or run a long process without risking the host machine, or wants to wire code execution into a LangChain, CrewAI, or custom agent loop.
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。
Arize is an AI observability platform, and Phoenix is its open-source tracing and evaluation tool for LLM applications. Use this skill when the user wants to trace OpenAI or LangChain calls, run a local Phoenix server, score RAG answers for faithfulness, debug slow or wrong LLM responses, or send OpenTelemetry traces to Arize for production monitoring.
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。
Authoritative reference for the neo4j-agent-memory Python package — a graph-native memory system for AI agents built on Neo4j — and for the hosted service (NAMS) at memory.neo4jlabs.com. Use this skill whenever the user mentions neo4j-agent-memory, agent memory with Neo4j, context graphs, the POLE+O model, MemoryClient/MemorySettings, the memory MCP server, or any of the framework integrations (LangChain, PydanticAI, CrewAI, AWS Strands, Google ADK, Microsoft Agent Framework, OpenAI Agents, LlamaIndex). Also use when the user mentions the hosted service at memory.neo4jlabs.com, NAMS, the Neo4j Agent Memory Service, the `nams_` API key prefix, or the hosted MCP endpoint. Also use when writing documentation, blog posts, tutorials, PRDs, or code samples for the project, when comparing agent memory approaches, or when positioning graph-native memory against vector-only approaches — even if the user doesn't explicitly name the package.
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 GraphRAG retrieval pipelines on Neo4j using the neo4j-graphrag Python package (v1.22.0+). Covers retriever selection (VectorRetriever, HybridRetriever, VectorCypherRetriever, HybridCypherRetriever, Text2CypherRetriever, ToolsRetriever), external vector DB retrievers (Weaviate, Pinecone, Qdrant), retrieval_query Cypher fragments, query_params, filters, GraphRAG pipeline wiring (GraphRAG + LLM + prompt), all LLM providers (OpenAI, Anthropic, Gemini/VertexAI, Bedrock, Cohere, Mistral, Ollama), embedder setup, index creation, token usage tracking, Cypher 25 SEARCH clause, and LangChain/LlamaIndex integration. Does NOT handle KG construction — use neo4j-document-import-skill. Does NOT handle plain vector search — use neo4j-vector-index-skill. Does NOT handle GDS analytics — use neo4j-gds-skill. Does NOT handle agent memory — use neo4j-agent-memory-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。
Ingests unstructured and semi-structured documents into Neo4j as a knowledge graph. Use when chunking PDFs, HTML, plain text, or Markdown; extracting entities and relationships from text with an LLM (SimpleKGPipeline, neo4j-graphrag); loading JSON via apoc.load.json; building Document→Chunk→Entity graph structures; or connecting LangChain/LlamaIndex document loaders to Neo4j. Covers neo4j-graphrag SimpleKGPipeline, LLM Graph Builder web UI, entity resolution, chunking strategies, and graph schema design for RAG pipelines. Does NOT handle structured CSV/relational import — use neo4j-import-skill. Does NOT handle GraphRAG retrieval after ingestion — use neo4j-graphrag-skill. Does NOT handle vector index creation — use neo4j-vector-search-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。
Plan how to build with You.com. Use when adding web search, content extraction, or research to an app or agent: finds the right You.com API, MCP server, SDK, or docs path (for example Vercel AI SDK, LangChain, Python SDK, or REST) through the `you-discover` tool and Docs MCP.
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。
设计 AI 架构，编写 Prompt，构建 RAG 系统和 LangChain 应用
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。
Imported skill agent from langchain
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 workloads from Google Cloud Platform to AWS — including AI and agentic workloads regardless of cloud provider. Triggers on: migrate from GCP, GCP to AWS, move off Google Cloud, migrate Terraform to AWS, migrate Cloud SQL to RDS, migrate GKE to EKS, migrate Cloud Run to Fargate, Google Cloud migration, migrate from OpenAI to Bedrock, move off OpenAI, switch from ChatGPT API to AWS, migrate from Gemini to Bedrock, migrate LangChain to Bedrock, migrate LangGraph to AWS, migrate agentic workloads to AWS, move AI workloads to AWS, migrate my AI app to AWS. Runs a 6-phase process: discover GCP resources from Terraform files, app code, or billing exports, clarify migration requirements, design AWS architecture, estimate costs, generate migration artifacts, and collect optional feedback. Clarify must finish before Design, Estimate, or Generate. Includes AI provider migration guidance (for example, OpenAI to Amazon Bedrock) by selecting closest-fit Bedrock model families for required modality, latency/quality targets, context windows, and cost constraints. Model mapping is compatibility-guided, not 1:1 parity; validate prompts, tool-calling behavior, and eval metrics before cutover. Do not use for: Azure or on-premises migrations to AWS, AWS-to-GCP reverse migration, general AWS architecture advice without migration intent, GCP-to-GCP refactoring, or multi-cloud deployments that do not involve migrating off GCP.
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 SurrealDB 3 architect and developer skill (tracks v3.1.4+). SurrealQL mastery, multi-model data modeling (document, graph, vector, time-series, geospatial), schema design, security, deployment, performance tuning, SDK integration (JS, Python, Go, Rust, Java, .NET, C, PHP, Swift, Kotlin, Ruby), Surrealism WASM extensions, SurrealML scope coverage (preview), built-in MCP (v3.1+) and standalone SurrealMCP, LangChain Python integration, editor tooling (LSP v0.1.6, tree-sitter, CodeMirror v1.0.6, VS Code/JetBrains/Neovim/Zed), and ecosystem integrations (Surrealist v3.9, Surreal-Sync, SurrealFS, SurrealKit v0.7, n8n, Agent Skills, setup-surreal). Universal skill for 30+ AI agents.
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。
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.
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。
Set up the prerequisites for the Quarkus + LangChain4j agentic stack in the coding agent you are running — detect and (with approval) install the toolchain, register the Quarkus Agents MCP and context7 MCP servers, and write the conventions file into your project. User-invoked only, via /setup-agentic-scaffolding; it prepares the environment the /scaffold-project and /audit-project skills depend on.
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。
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.
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 LLM applications with chain-style orchestration frameworks — prompts, chains, tool-calling agents, memory, and retrieval pipelines. Use when structuring multi-step LLM programs, adding tools to a model, or composing RAG systems 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。
Debug LangChain and LangGraph agents by fetching execution traces from LangSmith. Analyze agent behavior, investigate errors, and review tool calls and performance metrics.
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。
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. Deep comparison of A2A protocol, MCP, OpenAI function calling, and LangChain message types. Teaches when to use rigid schemas vs free-form with validation, typed vs untyped, streaming vs 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。
Especialista em Langfuse - plataforma de observabilidade LLM de código aberto. Cobre rastreamento, gerenciamento de prompts, avaliação, datasets e integração com LangChain, LlamaIndex e OpenAI. Essencial para depuração, monitoramento e melhoria de aplicações LLM em produção. Use quando: langfuse, observabilidade llm, rastreamento llm, gerenciamento de prompts, avaliação llm.
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。