Skill UI

This framework manages the ingestion, organization, synchronization, and search of knowledge across multiple storage layers, including local files, persistent memory, vector stores, and Git repositories. It provides a structured approach for ensuring knowledge durability, deduplication, and maintaining a 'single source of truth' across complex development and operational contexts.

This skill automatically generates an interactive SQL Notebook designed to validate changes in dbt models or snapshots. It processes changes identified from a GitHub PR or local repository, generating comparison queries that compare 'before' (production) and 'after' (development) data states. It is essential for embedding data observability and quality checks into CI/CD pipelines.

This skill provides a comprehensive framework for evaluating Power BI reports. It assesses report quality, usage adoption rates, performance metrics, and structural integrity across different lifecycle stages (development, testing, active use). It analyzes usage data, audience reach, and performance bottlenecks, generating actionable feedback and optimization recommendations to ensure reports are effective and maintainable.

This framework manages the ingestion, organization, synchronization, and search of knowledge across multiple storage layers, including local files, persistent memory, vector stores, and Git repositories. It provides a structured approach for ensuring knowledge durability, deduplication, and maintaining a 'single source of truth' across complex development and operational contexts.

This tool allows users to export comprehensive marketing data—including KPIs, contact records, campaign results, and performance snapshots—to external data stores. It supports integration with BigQuery for advanced data warehousing, Google Sheets for stakeholder collaboration, and Supabase for custom application development. The process includes mandatory PII handling, schema transformation, and detailed execution summaries to ensure compliance and data integrity for downstream analytics and reporting.

A comprehensive guide for authors aiming to publish agronomy or farming systems research in journals focused on sustainability. It outlines the critical need for making sustainability contributions explicit, measurable, and evidence-based (environmental, economic, and social). Use this tool to reframe your methodology, scope, and findings to meet rigorous academic standards for sustainable development.

This guide outlines the scope and submission requirements for the journal China Sport Science and Technology. It focuses specifically on application-oriented exercise science, covering competitive training, physical conditioning, physiological monitoring, technical analysis, and sports rehabilitation. It helps researchers determine the best fit for their work, distinguishing between purely theoretical research and practice-guided, evidence-based studies for athletic development.

A comprehensive guide for researchers in Developmental Psychology on meeting rigorous Open Science standards. It details best practices for data and materials sharing using Persistent Identifiers (DOIs), mandatory preregistration of hypotheses, sample-size justification, and adhering to ethical requirements when working with minors and vulnerable populations. It ensures compliance and reproducibility in modern scientific reporting.

A comprehensive guide for conducting rigorous empirical analysis on field and survey data in low- and middle-income settings. It covers advanced methods for handling clustered sampling, measurement error, differential attrition, and treatment-effect heterogeneity, ensuring results meet the high standards of reproducibility and robustness required by leading developmental economics journals.

This comprehensive guide establishes the rigorous methodological standards for developing credible causal identification strategies in development economics. It details best practices for modern quasi-experimental methods, including RCTs, DID, IV, and RDD. Authors learn how to meet the high empirical norms required by top-tier journals by ensuring robustness, addressing biases (e.g., TWFE), and validating external validity.

This guide provides best practices for presenting empirical results in development economics, emphasizing a 'figure-forward' approach suitable for top-tier journals. It teaches researchers how to transform complex statistical models (like DID, RDD, and ITT) into clear, self-contained, and impactful visual exhibits (plots and maps). The goal is to ensure that the headline findings are immediately legible to expert referees, moving beyond traditional, complex coefficient tables.

A comprehensive guide for ensuring the robustness of empirical findings, particularly for submissions to top-tier economic journals. This method moves beyond simply showing that coefficients remain significant, focusing instead on defending the core economic interpretation by systematically ruling out alternative mechanisms, structural assumptions, and potential sources of bias. It guides the development of a rigorous 'robustness battery.'