Skill UI

This guide provides essential knowledge for handling common errors encountered when integrating with Veeva Vault. It covers REST API usage, VQL querying patterns, and VAPIL Java SDK concepts, helping developers manage document lifecycles and resolve issues like invalid sessions, insufficient permissions, or data validation conflicts in a life sciences CRM environment.

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.

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.

A powerful skill for searching and retrieving comprehensive academic metadata using the OpenAlex API. It provides a unified view of research papers, including full open citation graphs, detailed institutional affiliations, and crucial funding information (e.g., NSF, NIH). Ideal for deep academic research, comparative literature reviews, and identifying research trends across multiple disciplines.

This utility evaluates complex arithmetic expressions using arbitrary-precision decimal mathematics. It is specifically designed for scientific, financial, or engineering calculations where standard floating-point arithmetic causes unacceptable rounding errors (e.g., 0.1 + 0.2). Supports addition, subtraction, multiplication, division, exponents, and parentheses.

A systematic, multi-phase guide for tackling hard bugs and performance regressions. It mandates building a robust feedback loop (e.g., failing tests, harnesses), formulating falsifiable hypotheses, targeted instrumentation, and rigorous regression testing. This methodology ensures that debugging efforts are structured, efficient, and highly reproducible, making it ideal for complex software defects.

A comprehensive server providing research-grade access to arXiv. Users can search papers by topic, author, or date, download full texts, and analyze the scientific landscape by traversing citation graphs. It also supports local semantic search over downloaded documents and enables monitoring for new papers with real-time alerts. Ideal for academic research and literature review.

This powerful web toolkit uses Exa to perform advanced web searches and detailed URL content extraction. It is specifically tuned for scientific, technical, and academic research, allowing users to filter results by scholarly categories (e.g., research papers) and include academic domains. Use it for complex lookups, gathering current information, or batch extracting content from articles and PDFs.

This skill enforces disciplined SLO definition based on SRE principles, ensuring that reliability promises are measurable and actionable. It guides users in selecting the right SLI (Service Level Indicator), accurately calculating error budgets, setting multi-window burn-rate alerts, and reviewing existing SLO documents for common architectural flaws (e.g., targets too aggressive, wrong SLI selection). Ideal for defining, reviewing, and maintaining service reliability commitments.

Diagnoses full-stack failure modes in agent and LLM applications, including wrapper regressions, memory contamination, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings and code-first fixes for production agent systems.

StyleSeed is a comprehensive design judgment engine designed to elevate the aesthetic quality of AI-generated user interfaces. It codifies 69 visual rules covering color discipline, spatial rhythm, information hierarchy, and motion feedback. By integrating these principles, developers can ensure that AI output looks professionally designed and polished, rather than simply generated. Includes React components, Tailwind CSS implementation, and actionable design guidelines.