Skill UI

Produces production-ready Dataverse SDK code for Python, showcasing advanced error handling and retries, batch operations, OData query tuning, metadata lifecycle management, configuration/timeouts, cache invalidation, chunked file uploads, and Pandas integrations with references.

Master the art of developing robust and scalable Python applications that interface with Microsoft Dataverse. This skill covers implementing enterprise-grade best practices, including comprehensive error handling using DataverseError hierarchy, advanced retry logic with exponential backoff, and optimizing API calls using OData standards (filtering, selecting, and expanding). It ensures code is clean, maintainable, and production-ready.

Provides Dataverse Python SDK snippets for installation, interactive authentication, CRUD, bulk operations, paging, and optional file uploads aligned with official guidance.

Guides architects through Dataverse SDK use cases by analyzing requirements, designing data models, selecting integration patterns, and producing production-ready Python code with best practices.

This skill provides comprehensive governance for Power Automate flows and Power Apps. It allows users to classify flows by business impact, detect orphaned resources, audit connectors, and enforce compliance by writing essential metadata to a cached store. It is ideal for IT governance teams needing centralized control and deep insight into automation assets without relying on Dataverse or the CoE Starter Kit.

A comprehensive guide for authors preparing replication and transparency packages for submissions to the British Journal of Political Science (BJPS). It outlines requirements for depositing quantitative and qualitative data and code into the BJPolS Dataverse, ensuring that research findings are fully reproducible and adhering to the Data Access and Research Transparency (DA-RT) statement.

This guide outlines the mandatory data and code requirements for publishing manuscripts in International Organizations (IOs). It details the rigorous process—from conditional acceptance to final publication—which requires authors to prepare complete, reproducible packages. Key steps include submitting data/code, undergoing external re-running of quantitative results, verifying formal model proofs, and depositing materials with a DOI in the IO Dataverse.

A mandatory guide for authors submitting to the Journal of Financial and Quantitative Analysis (JFQA). This policy dictates the required archive structure, ensuring that all reported findings can be fully reproduced by depositing source code, raw or pseudo datasets, and a detailed execution roadmap in the JFQA Dataverse. It guarantees the highest standards of academic transparency and reproducibility in financial research.

A comprehensive checklist and guide for authors preparing manuscripts for scholarly submission, specifically for Public Opinion Quarterly (POQ) via ScholarOne. It ensures compliance with double-blind anonymity, specific word count caps, mandatory AAPOR disclosure elements (Appendix A), and proper data archiving protocols (Dataverse). Use this guide as the final critical check before clicking 'Submit'.

This guide outlines the rigorous data and transparency requirements for submitting academic manuscripts, particularly for social science journals. It details two core components: methodological disclosure via 'Appendix A: Disclosure Elements' and building a complete replication package. Authors must ensure all published tables and figures can be reproduced exactly using deposited data and code in a permanent archive (Dataverse) before the paper advances to typesetting.

A comprehensive guide for academic authors adopting open science standards, based on TOP Guidelines. It outlines best practices for ensuring research reproducibility, covering data citation, data provenance documentation, data sharing via repositories (Dataverse/QDR), and managing restricted-data paths for various evidence types.

This guide outlines the mandatory requirements for authors publishing quantitative or mixed-method research in academic journals. It details the process of creating a complete replication package—including raw data, master scripts, seeds, and extensive README documentation—and depositing it in a trusted data repository (like Dataverse). It ensures scholarly rigor by guaranteeing that published findings are fully reproducible and traceable.