Skill UI

This expert tool guides users through rigorous statistical analysis, specializing in A/B testing, hypothesis testing, and interpreting experimental results. It helps define sample sizes, executes appropriate tests (Z-test, T-test, Chi-square), and critically distinguishes between 'statistical significance' and 'practical significance' to ensure data-driven decision-making for product and business strategy.

This guide provides comprehensive technical instructions for optimizing Palantir Foundry costs. Learn to use incremental transforms to drastically reduce compute rebuilds, right-size Spark profiles based on data volume, and migrate from expensive API polling to efficient, event-driven webhooks, ensuring both cost efficiency and system stability.

This skill generates a sophisticated, single-screen consumer dashboard mimicking a high-end lifestyle publication's dating column. It moves beyond simple swiping mechanics to visualize complex social metrics, mutual matches, and connectivity trends using editorial typography and restrained design. Ideal for concepts requiring data-driven storytelling about personal or community relationships.

Analyzes options chain data by separating real calls and puts from lottery-like speculative contracts to produce more reliable put/call sentiment, anomaly detection, and expiry breakdowns for watchlists, sectors, and event-driven names.

This skill generates a comprehensive, structured churn analysis, moving far beyond simple headline rates. It meticulously separates avoidable vs. unavoidable customer losses, identifies high-risk segments (by tier, cohort, etc.), calculates critical metrics like Net Revenue Retention (NRR), and pinpoints actionable early warning signals. The output culminates in prioritized, data-driven intervention recommendations to improve customer retention.

Execute arbitrary PySpark or pure Python code directly on Fabric Spark compute using the Livy API. This method creates ephemeral sessions without generating persistent notebook artifacts. It provides full read/write access to lakehouse Delta tables, making it ideal for agent-driven ETL pipelines, quick data transformations, or analyses where session cleanup is critical. Users must manage the session lifecycle (create, run, and delete).

This guide outlines rigorous standards for conducting heterogeneity analysis in economic research. It stresses that heterogeneity should not merely involve slicing data by region or size, but must be theoretically driven, linking multiple dimensions (e.g., ownership, financing constraints, industry type) to core economic mechanisms. The analysis must demonstrate significant group differences and clearly connect findings to differential policy implications, elevating the discussion from mere observation to actionable policy science.

This comprehensive checklist guides authors through the final pre-submission review process for demanding academic journals. It ensures the manuscript is scientifically rigorous and complete, focusing on critical areas such as model-driven theoretical advancement, model comparison, data reproducibility (depositing code and verifying fit regeneration), and adherence to open science mandates. Use this guide before uploading to the journal's portal.

This tool serves as an orchestrator for authors preparing submissions to Cognitive Psychology (Elsevier). It guides users through the entire research lifecycle—from initial topic selection and theoretical formalization to study design, data analysis, and open science practices. It emphasizes structuring the contribution as a model-driven, multi-experiment program, ensuring all necessary steps, including code transparency, are addressed.

This comprehensive guide provides rigorous econometric strategies for establishing parameter identification, crucial for structural modeling and causal inference. It details how to prove that model parameters are uniquely determined by the observed data, moving beyond mere estimation convergence or external calibration. Essential for advanced empirical research and addressing stringent peer review standards.

This guide provides an advanced framework for conducting rigorous causal inference and policy evaluation, essential for academic journals. It covers state-of-the-art methods including DiD, RDD, Synthetic Control, and experimental analysis. The core focus is moving beyond simple p-values to estimate effect magnitudes in tangible 'decision units' (e.g., dollars per household, percentage points). It mandates comprehensive stress-testing, including pre-trend checks, placebo outcomes, and detailed subgroup heterogeneity analysis, ensuring results are immediately actionable by regulators.

This skill automates the creation of professional, multi-element Excel dashboards from raw CSV or tabular data. It structures the output with a dedicated 'Dashboard sheet' that serves as the primary landing view. Features include formula-driven KPI cards, linked charts, sparklines, and semantic conditional formatting. It is ideal for generating executive, analytics, or performance dashboards, ensuring that the user sees immediate, actionable insights without needing to navigate raw data sheets.