Skill UI

A comprehensive tool for generating publication-quality figures essential for Machine Learning and AI academic papers. It supports two distinct workflows: generating complex system architecture and workflow diagrams using AI (Gemini), and creating precise, data-driven visualizations (line charts, heatmaps, etc.) using standard libraries like matplotlib and seaborn. Use it when preparing any figure for a conference submission.

Provides programmatic access to massive, versioned public single-cell and spatial transcriptomics datasets from CZ CELLxGENE. This skill allows users to efficiently query specific subsets of cell metadata, gene expression matrices, embeddings, and summary statistics across various organisms, tissues, and diseases. It is ideal for large-scale, memory-efficient biological research and model training without full dataset downloads.

Generates professional, publication-ready clinical decision support (CDS) documents for pharmaceutical and clinical research settings. Capabilities include biomarker-stratified patient cohort analyses, evidence-based treatment recommendation reports (with GRADE grading), and advanced statistical analyses (e.g., hazard ratios, survival curves). Outputs are optimized for regulatory submissions and medical guidelines.

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).

This skill provides a highly rigorous framework for analyzing complex public issues, platform governance, and institutional compliance. It is designed to structure evidence boundaries, assess procedural justice, and track public commitments by synthesizing source ledgers and multiple data points. It is intended as a specialized sub-routine triggered by the main CrossFrame suite, not for standalone use. Key focus areas include source verification, identifying weak signals, and mitigating AI compliance performance risks.

This skill scrapes Substack Notes analytics by extracting key engagement metrics (likes, comments, restacks) and content details. It overcomes the lack of a public API, allowing users to analyze performance data from a specified date range into a highly formatted, actionable Excel spreadsheet. The output includes detailed per-note data, total engagement calculations, and summary statistics.