Skill UI

This comprehensive guide outlines the process of migrating analytics data from major platforms like Google Analytics, Mixpanel, Amplitude, or Segment to PostHog. It covers implementing a dual-write strategy, mapping historical event names and properties, and performing bulk historical data imports, ensuring a smooth transition and data continuity.

Transforms data into publication-ready figures with multi-panel layouts, precise annotations, colorblind-friendly palettes, and journal formatting using matplotlib, seaborn, and plotly plus export helpers for PDF/EPS/TIFF.

A comprehensive guide to product analytics, covering essential concepts like event tracking (PostHog, Mixpanel), funnels, cohort analysis, retention metrics, North Star Metrics, and OKR setting. Use this skill to define data-driven product strategies, optimize user journeys, and build robust performance dashboards.

Generate publication-quality figures and tables from experiment data. Supports line plots, bar charts, scatter plots, heatmaps, box/violin plots, and multi-panel figures for academic papers. Uses consistent matplotlib styling, high-resolution PDF/PNG output, and can optionally review figures via Codex MCP.

A comprehensive automation toolkit for Mixpanel product analytics, accessible via Rube MCP. Automate key data tasks including event aggregation, detailed segmentation, conversion funnel analysis, user profile management, cohort tracking, and running custom JQL queries. Ideal for data analysts and product managers needing to deeply analyze user behavior patterns.

Matplotlib is a foundational Python library for creating static, animated, and interactive plots with full customization. It supports various plot types like line, scatter, bar, and heatmaps, suitable for scientific and statistical visualizations. Users can customize appearance, create multi-panel figures, and export to PNG, PDF, or SVG. Ideal for publication-quality graphics and integrating with Jupyter notebooks.

Seaborn is a Python visualization library built on matplotlib for creating statistical graphics. It integrates with pandas for dataset-oriented plotting, offering publication-quality defaults for distributions, relationships, and categorical comparisons. Ideal for exploratory data analysis, multivariate analysis, and complex multi-panel figures. Supports various plot types like box plots, violin plots, and heatmaps.

This guide provides comprehensive methodological frameworks for designing rigorous empirical studies suitable for top-tier academic journals. It helps researchers match theoretical claims (e.g., causal effects, cross-level dynamics) to appropriate designs (experiments, panel data, qualitative analysis). It details how to proactively address major threats like Common-Method Bias, Endogeneity, and inconsistent levels of analysis, ensuring high internal and external validity.

A comprehensive guide detailing the selection of appropriate statistical estimators (e.g., DiD, Panel FE, ANOVA) for various research designs. It also outlines rigorous best practices for ensuring data integrity, conducting robustness checks, and adhering to journal code-sharing policies, ensuring highly reproducible empirical research.

This guide provides comprehensive guidelines for selecting, validating, and defending rigorous research designs for Contemporary Accounting Research (CAR) manuscripts. It covers various approaches—including panel archival, controlled experiments, analytical models, field studies, and surveys—while stressing critical rigor checks like identification strategy, internal validity, and model discipline. It also mandates adherence to ethical review processes and data integrity policies.

A detailed guide for scrutinizing statistical inference in high-level econometric manuscripts, designed to withstand rigorous technical peer review. It covers critical areas such as selecting appropriate clustering levels, handling weak instrumental variables (IV), correcting for multiple hypothesis testing, and ensuring accurate standard error reporting for panel data and causal inference models.

Provides a comprehensive framework for defending the methodological rigor of comparative social science research. This skill guides users through establishing comparative leverage, whether in cross-national panel data, case-based comparisons, or natural experiments. It emphasizes techniques like causal inference, process tracing, and ruling out rival theoretical accounts to strengthen the study's claims.