Skill UI

This guide details how to perform authentication and data operations with the Veeva Vault platform using its REST API. It covers obtaining session IDs via username/password or OAuth 2.0, and executing complex queries using VQL (Veeva Query Language). Ideal for developers integrating document management systems in the life sciences sector.

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.

Provides comprehensive, enterprise-grade patterns for setting up and managing Veeva Vault across multiple environments. This guide is essential for implementing advanced, compliant deployment strategies in the regulated life sciences sector, covering API usage (VQL, VAPIL) and best practices for data validation and security.

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.

This comprehensive guide serves as a checklist for interacting with Veeva Vault in a production environment. It details best practices for using the REST API, executing complex VQL queries, and integrating with the VAPIL Java SDK. Coverage includes authentication flows, common data patterns, document lifecycle management, and essential error handling procedures for life sciences and CRM operations.

This guide provides comprehensive reference architecture for integrating with Veeva Vault, covering REST APIs, VQL query language, and VAPIL Java SDK. It details key concepts like authentication, common data patterns, and error handling, making it essential for developers working on life sciences or CRM solutions built on the Vault platform.

Hugging Science is a curated, LLM-friendly index specializing in AI/ML applications across diverse scientific domains, including biology, chemistry, physics, and medicine. It helps researchers discover pre-filtered datasets, state-of-the-art models, and interactive demos for solving complex scientific problems, ensuring high signal quality beyond generic search.

The BIDS skill provides a comprehensive framework for organizing, standardizing, and managing complex neuroscience and biomedical datasets. It covers querying, validating, and converting various modalities (MRI, EEG, MEG, etc.) to ensure compliance with the BIDS standard for research reproducibility and efficient data sharing.

This skill converts complex, narrative technical documents, research excerpts, or natural language problem descriptions into a highly structured Mathematical Problem Specification (MPS). It systematically extracts variables, constraints, objectives, and uncertainty parameters. By adhering to a strict Zero-Inference Protocol, it ensures all extracted elements are strictly grounded in the source text, making it invaluable for formalizing real-world optimization, data science, and engineering problems.

A specialized tool for scientific data visualization, acting as a visualization advisor to analyze data, recommend chart types, prevent common errors, and generate publication-grade figures.

A comprehensive guide for strengthening the methodological design of advanced geographical manuscripts. It covers various paradigms including spatial/quantitative GIScience, remote sensing, qualitative human geography, and mixed methods. Key advice areas include rigorously addressing spatial dependence (MAUP), validating measurements, justifying case selection, and ensuring holistic integration of diverse data sources.

A comprehensive guide for authors submitting data mining papers to the ACM SIGKDD Conference. It helps authors assess the manuscript's fit, reframe the contribution (e.g., from journal to conference style), and strengthens the evidence bar by focusing on novelty, scale, reproducibility, and clear real-world impact required by top-tier data science venues.