148 个 · 按相关度排序
Create documents when several research or execution tools fail. Delegate PDFs and complex layouts to shell_agent, a separate agent, or save Markdown directly, while distinguishing verified facts from general guidance and checking the output.
中文简介正在准备中。当前显示英语简介。
Keep document creation moving when web searches and page retrieval repeatedly fail. The skill preserves the original task, uses the model’s existing knowledge, and clearly identifies information that could not be checked against external sources.
中文简介正在准备中。当前显示英语简介。
Produce Word, PDF, and HTML documents when retrieval or conversion tools fail. The workflow switches to available knowledge with stated limitations, prepares characters for PDF conversion, tries alternative tools, and checks the resulting files.
中文简介正在准备中。当前显示英语简介。
Select technologies for AI and machine learning features in .NET 8+ applications. Get task-specific plans or implementation guidance for structured predictions, text generation, document retrieval, tool-using agents, and running pretrained models locally or in production.
中文简介正在准备中。当前显示英语简介。
Compliant, retrieval-grounded research over investor-State dispute settlement (ISDS) awards and decisions. Use when the user asks about ICSID / investment-treaty arbitration cases, awards, or doctrines (fair and equitable treatment, expropriation, jurisdiction, costs, annulment, etc.) and wants answers grounded in the actual document text with pinpoint citations. Identifies the correct document on the case page, confirms it against the PDF's own first pages, retrieves primary documents on demand from ICSID, PCA, etc., never scrapes or hosts a corpus in violation of applicable terms, and cites only retrieved text.
中文简介正在准备中。当前显示原文说明。
Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.
中文简介正在准备中。当前显示原文说明。
Implements document chunking, embedding generation, vector storage, and retrieval pipelines for Retrieval-Augmented Generation systems. Use when building RAG applications, creating document Q&A systems, or integrating AI with knowledge bases.
中文简介正在准备中。当前显示原文说明。
Build RAG / unstructured-document evaluation datasets and demo documents (e.g. for Knowledge Assistant) on Databricks: generate synthetic PDFs locally, upload to Unity Catalog volumes, and pair each document with test questions for retrieval evaluation.
中文简介正在准备中。当前显示原文说明。
Implements document chunking, embedding generation, vector storage, and retrieval pipelines for Retrieval-Augmented Generation systems. Use when building RAG applications, creating document Q&A systems, or integrating AI with knowledge bases.
中文简介正在准备中。当前显示原文说明。
Split documents for retrieval effectively — fixed, semantic, structural, and hierarchical chunking with overlap tuning. Use when RAG retrieval quality depends on how documents are divided.
中文简介正在准备中。当前显示原文说明。
Interact with Langfuse and access its documentation. Use when needing to (1) query or modify Langfuse data programmatically via the CLI — traces, prompts, datasets, scores, sessions, and any other API resource, (2) look up Langfuse documentation, concepts, integration guides, or SDK usage, or (3) understand how any Langfuse feature works. This skill covers CLI-based API access (via npx) and multiple documentation retrieval methods.
中文简介正在准备中。当前显示原文说明。
Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. Use when: building RAG, vector search, embeddings, semantic search, document retrieval.
中文简介正在准备中。当前显示原文说明。
Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.
中文简介正在准备中。当前显示原文说明。
Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.
中文简介正在准备中。当前显示原文说明。
Designs retrieval-augmented generation pipelines for document-based AI assistants. Includes chunking strategies, metadata schemas, retrieval algorithms, reranking, and evaluation plans. Use when building "RAG systems", "document search", "semantic search", or "knowledge bases".
中文简介正在准备中。当前显示原文说明。
将文档、会话和项目信息分类保存到合适的位置，跨本地文件、记忆工具、知识库和数据库查重、同步与检索，并维护索引和摘要。
使用Pinecone构建按内容含义查找信息的搜索基础设施，支持数据写入、更新与删除、条件筛选，以及语义搜索与关键词搜索的组合。
Design knowledge systems that preserve document structure and connections across sources. Choose retrieval methods, organize conversation memory, and plan evidence-based updates, review workflows, and access controls for information that scattered text searches cannot adequately cover.
中文简介正在准备中。当前显示英语简介。
Design and improve retrieval for RAG, which supplies external knowledge to AI answers. Cover document splitting, semantic and keyword search, combined result ranking, and quality measurements to identify where relevant information is being missed.
中文简介正在准备中。当前显示英语简介。
Consult bundled technical documentation to understand and integrate Hindsight, a memory system for AI agents. Find guidance on storing and retrieving memories, API calls, configuration, deployment, SDKs, troubleshooting, and performance.
中文简介正在准备中。当前显示英语简介。
Design and implement LLM applications, AI agents, and RAG systems that retrieve documents to support answers. Cover model selection, search quality, testing, monitoring, safety measures, and cost controls for applications intended for production use.
中文简介正在准备中。当前显示英语简介。
Plan AI application development by selecting relevant phases and companion skills. The workflow covers language model integration, retrieval from documents, agents, machine learning pipelines, monitoring, and security, with prompts and quality checklists.
中文简介正在准备中。当前显示英语简介。
Investigate prompt injection in sandboxed security challenges by tracing how untrusted text changes an agent’s decisions or tool calls. Build a minimal reproduction and preserve evidence showing where external content crosses into trusted instructions or actions.
中文简介正在准备中。当前显示英语简介。
Design AI applications with LangChain 1.x and LangGraph, libraries for coordinating language models and workflows. Build agents that use external tools, manage conversation history and saved state, process documents, and support tracing and testing.
中文简介正在准备中。当前显示英语简介。