147 skills · sorted by relevance
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.
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.
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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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".
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Save, organize, synchronize, and retrieve documents, conversations, and project information across local files, memory tools, knowledge repositories, and databases. Check for duplicates before storing information and choose a suitable home for each type of knowledge.
Build search and retrieval systems with Pinecone, a managed database for numerical representations of content. Covers data uploads, filtered similarity searches, combined semantic and keyword search, and connections to LangChain and LlamaIndex.
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.
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.
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.
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.
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.
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.
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.
Build AI applications with LangChain, including chatbots with conversation history and systems that search documents before answering. The skill provides examples for connecting models, calling tools, retrieving information, and tracing application runs.
Turn plain language requirements into SQL, investigate slow queries, document database structures, and prepare migration templates with rollback plans. Support everyday database development and ORM use, where libraries connect application code to database tables.