Skill UI

Provides an enterprise-grade incident runbook for Veeva Vault deployments, tailored for regulated life sciences environments. This guide details best practices for handling critical operational incidents, ensuring compliance with rigorous standards, and utilizing advanced features like VQL and VAPIL for robust data retrieval and error resolution.

A comprehensive guide for the Veeva Vault local development loop. It details how to interact with the platform using REST APIs, advanced VQL queries for data retrieval, and the VAPIL Java SDK. Ideal for developers implementing solutions involving document management, clinical operations, and CRM processes within the Veeva ecosystem.

This guide provides deep-dive patterns and best practices for migrating enterprise data into Veeva Vault. It is essential for developers implementing advanced, compliant, and regulated workflows within the life sciences sector. Key considerations include validating API changes against strict compliance requirements, utilizing VQL for efficient data retrieval, and handling complex error scenarios during migration.

This skill provides enterprise-grade observability patterns specifically for Veeva Vault deployments. It guides users through implementing advanced patterns, utilizing VQL for efficient data retrieval, and ensuring compliance in regulated life sciences environments. Use this when building robust, observable applications within the Veeva ecosystem.

A comprehensive guide for integrating Microsoft Graph SDK into applications. It covers accessing Microsoft 365 data (users, mail, calendar, Teams, SharePoint) from various languages (.NET, Python, TypeScript). Key topics include robust authentication patterns (Managed Identity, OBO), advanced data retrieval techniques like pagination, delta queries, batch requests, and setting up change notifications via webhooks. Learn best practices for secure and efficient API interaction.

A powerful tool for deterministic and reproducible retrieval of evidence from specialized academic and policy corpora. It supports searching arXiv (preprints), PubMed/PMC (biomedical literature), and US policy documents. Users can check corpus freshness, retrieve stable metadata, and fetch full-document Markdown for high-reliability research tasks.

Use the HasData CLI to access real-time, structured web data from major platforms. This tool provides dedicated subcommands for various APIs (including Google SERP, Amazon, Zillow, Booking, etc.), enabling users to automate data collection, scrape specific URLs, and retrieve structured information like local business listings, product pricing, and real estate data directly from the command line.

A lightweight tool designed to fetch data from a single URL via HTTP/HTTPS using various methods (GET, POST, PUT, DELETE). It serves as a fast, direct shell wrapper, ideal for replacing complex agent steps that only require simple data retrieval. Supports configurable timeouts and response body capping, making it perfect for quick data fetching in meta-skill workflows.

This skill provides a comprehensive guide for building scalable Retrieval-Augmented Generation (RAG) pipelines and persistent agent memory using Pinecone as the vector database. It covers index setup, embedding large corpuses, implementing dense and hybrid semantic search, and advanced metadata filtering for real-world production systems. Use this for building searchable knowledge bases and context-aware AI agents.

This tool provides a comprehensive security assessment framework for Retrieval-Augmented Generation (RAG) systems and vector databases. It tests critical vulnerabilities such as embedding inversion, cross-tenant data leakage, knowledge-base poisoning, and retrieval manipulation, aligning with OWASP LLM08:2025 standards. It is essential for validating the security posture of multi-tenant and vector-search applications.

A comprehensive red-teaming skill for assessing the security posture of Retrieval-Augmented Generation (RAG) pipelines. It systematically probes two critical injection surfaces: poisoned retrieved context (indirect prompt injection) and embedding manipulation. Use this skill to validate retrieval guardrails, ensure data leak prevention, and demonstrate vulnerabilities in LLM-powered knowledge assistants.

This comprehensive skill provides an index of official blueprints and implementation guides for building full-stack, AI-powered applications using Weaviate. It covers advanced patterns such as Retrieval-Augmented Generation (RAG), agentic workflows, data exploration, and multimodal document search, guiding users through best practices for setup, clients, and frontends.