Skill UI

This technique helps users solve highly complex movie trivia or cinematic research questions that involve multiple, intertwined constraints (such as specific awards, historical eras, directors, or plot elements). Instead of attempting to input all variables at once, it teaches the methodology of decomposing the main question into 2-3 independent, searchable dimensions, ensuring a systematic and accurate search process.

A technique for dramatically increasing the hit rate when searching for TV series. Instead of using generic terms like genre or era, combine 2-3 highly distinctive plot details (e.g., character sacrifices, specific season incidents) into a single query. This method leverages the unique nature of plot points to pinpoint specific shows quickly.

DeepTools is a comprehensive command-line toolkit for processing and analyzing high-throughput sequencing data. It supports workflows for ChIP-seq, RNA-seq, ATAC-seq, and other NGS experiments. Core capabilities include converting BAM files to normalized coverage tracks (bigWig), performing quality control (QC), sample correlation analysis, and generating publication-quality visualizations, such as heatmaps and profile plots across genomic regions.

Matplotlib is Python's foundational library for creating highly customizable static, animated, and interactive visualizations. It supports various plot types (line, scatter, heatmap, 3D) and offers both pyplot and object-oriented interfaces. Ideal for generating publication-quality scientific charts, complex multi-panel figures, and integrating into notebooks.

A meta-skill dedicated to creating high-quality, publication-ready scientific figures. It guides users on best practices for journal submission, including multi-panel layouts, precise error bar inclusion, colorblind-safe palette usage, and adherence to specific journal styles (Nature, Science). It orchestrates libraries like matplotlib, seaborn, and plotly to ensure optimal resolution and format export (PDF, EPS, TIFF).

Seaborn is a high-level Python data visualization library built on Matplotlib, designed for creating aesthetically pleasing and statistically rich graphs. It excels in exploring multivariate relationships, visualizing distributions (like box and violin plots), and handling complex dataset-oriented statistical graphics with minimal code. Ideal for data exploration and generating publication-quality figures.

Generates high-quality, deterministic, and editable vector diagrams (SVG) for system architecture, workflows, and pipelines. By using a structured JSON specification (FigureSpec), it ensures that the same input always yields the identical output, making it ideal for reproducible technical documentation, academic papers, and formal system modeling where precise layout and connections are critical. It is optimized for non-data-plot, formal diagrams.

This technique is designed for the final verification stage when analyzing a candidate TV series. Instead of accepting initial matches, users must perform targeted searches combining the show name and specific character details (e.g., birth year, specific plot event, relationship). This ensures that the candidate show satisfies all complex constraints, eliminating false positives and confirming the accuracy of the plot details.

This tool reads a CSV file containing experimental results (x, y_baseline, y_ours). It utilizes matplotlib to generate a two-line comparative plot of these data points and outputs the visualization directly as a PDF file. It is highly useful for academic purposes, allowing users to easily compare baseline model performance against proposed methods for inclusion in research papers.

This skill enables the creation of advanced, highly customized visualizations directly within Power BI reports (PBIR). By utilizing powerful Python libraries like Matplotlib and Seaborn, users can process data via pandas DataFrames and render complex statistical charts (e.g., bar charts, line graphs) into static PNG images. It provides a structured workflow, covering script injection, validation, and best practices, significantly enhancing the depth and professionalism of business reports.

This skill guides the creation of advanced, publication-quality statistical visualizations within Power BI using the R programming language, primarily leveraging the ggplot2 package. It details the entire workflow, from structuring the visual JSON (using `pbir-format`) to writing the mandatory `ggplot2` script and correctly injecting it into the report. Ideal for data scientists needing to render complex, customized charts that surpass standard Power BI visuals. Static PNG output is generated, ensuring high fidelity for reports.