Skill UI

This advanced skill is an end-to-end tool for transforming complex research topics into professional, visualized knowledge graph PDFs. It automatically conducts multi-round web searches, extracts key entities, structures related knowledge, and designs the optimal visualization (supporting Graphviz, PlantUML, etc.). Ideal for deep academic analysis, mapping character relationships, or visualizing complex technical architectures where existing structured data is unavailable.

A comprehensive skill for creating, editing, and analyzing complex spreadsheets (.xlsx, .csv, etc.). This skill excels in financial modeling, ensuring all calculations are managed via dynamic Excel formulas rather than hardcoded values. It supports data analysis using pandas, rigorous formatting standards, and complex formula integrity checks, making it ideal for financial reporting and data visualization.

This guide details the process of creating advanced, static visualizations in Power BI using Python libraries like matplotlib and seaborn. It covers the full workflow, from script writing and data injection to best practices and limitations, enabling users to render complex, custom charts not supported by native Power BI visuals.

A comprehensive guide and workflow for embedding R scripts, primarily using the ggplot2 library, into Power BI Reports (PBIR). This tool enables the creation of high-quality, static, publication-ready statistical visualizations directly within the report canvas by executing R code on the defined dataset. It covers the full lifecycle: adding the visual, writing the script, injecting the code, and validating the output.

A specialized tool for scientific data visualization, acting as a visualization advisor to analyze data, recommend chart types, prevent common errors, and generate publication-grade figures.

Transform raw structured data (CSV, Excel, JSON) into professional, long-form, interactive visualization reports. This tool generates comprehensive business review pages featuring KPI dashboards, multiple dynamic charts (using Chart.js/ECharts), detailed data tables, and narrative business insights, making it ideal for weekly or monthly performance reporting.

Generates sophisticated, editorial-grade data visualizations inspired by major news publications like The New York Times. Ideal for presenting complex data (CSV/JSON) or key conclusions as single frames or animations for video explainers, social media posts, or data reports. Features clean design, specific typography, and staggered reveal animations.

A comprehensive guide for assembling a fully reproducible research package for macroeconomics papers targeting the American Economic Journal (AEJ: Macro). It ensures compliance with the AEA Data and Code Availability Policy by documenting raw data, simulation/calibration code, estimation routines, and visualization steps, guaranteeing that results can be independently verified by reviewers.

This guide provides rigorous guidelines for creating high-quality figures and tables for complex systems manuscripts, especially in agricultural science. It emphasizes shifting focus from single-factor bar charts to systemic visualizations that reveal interactions, trade-offs (e.g., Pareto fronts), model dynamics, resource flows (Sankey diagrams), and model performance (observed-vs-simulated fit). Exhibits must be self-contained, reproducible, and convey uncertainty.

A comprehensive guide for authors submitting to the American Political Science Review (APSR). It outlines best practices for creating self-contained, accessible, and statistically rigorous tables and figures. The resource emphasizes principles like maintaining word-count compliance, ensuring reproducibility, and presenting complex results (like treatment effects) using appropriate visualizations.

This guide outlines the rigorous standards for presenting tables and figures in Cognitive Psychology manuscripts. It emphasizes moving beyond simple bar charts of means, requiring authors to overlay the model's predicted curve onto observed data, reveal distributions and uncertainty intervals, and accurately report parameter estimates with confidence intervals. The goal is to ensure reproducible, comprehensive, and highly informative scientific communication.

A comprehensive guide for creating self-contained, accessible, and budget-compliant scientific exhibits (figures, tables, and maps) for manuscripts, particularly in conservation biology. It covers best practices for data visualization, adhering to journal standards, ensuring accessibility (e.g., grayscale compatibility), and masking sensitive geographical data.