Skill UI

Matplotlib is the foundational Python library for creating static, animated, and high-quality visualizations. This comprehensive skill guide covers everything from basic line plots and scatter graphs to advanced concepts like multi-subplot layouts, heatmaps, and 3D visualization. It details both the convenient pyplot interface and the powerful object-oriented API, ensuring you can generate publication-ready charts with professional customization.

NetworkX is a powerful Python package designed for creating, manipulating, and analyzing complex networks and graphs. It supports various graph types (Directed, Undirected, MultiGraphs) and provides extensive functionality for graph algorithms, including shortest path calculation, centrality measures (like PageRank), community detection, and connectivity checks. Users can generate synthetic networks, read/write data from multiple formats (GraphML, CSV, Pandas), and visualize results using Matplotlib. Ideal for social network analysis, biological research, and system modeling.

Polars is a lightning-fast DataFrame library designed for efficient, memory-efficient data processing in Python and Rust. Built upon Apache Arrow, it supports lazy evaluation and parallel execution, making it ideal for ETL pipelines, large-scale data transformations, and scenarios where standard Pandas performance is insufficient. It excels at handling datasets up to 100GB that fit within available RAM.

Scanpy is a powerful, scalable Python toolkit built on AnnData for comprehensive single-cell RNA-seq data analysis. It supports the complete biological workflow, including quality control, normalization, dimensionality reduction (PCA, UMAP, t-SNE), clustering, marker gene identification, and sophisticated cell type annotation, enabling the generation of publication-quality figures.

Scikit-learn is the industry-standard Python library for classical machine learning. It provides comprehensive tools for supervised and unsupervised learning tasks, including classification, regression, clustering, and dimensionality reduction. Use it to build robust, production-ready ML pipelines, perform rigorous model evaluation, and preprocess complex datasets for various data science applications.

SymPy is a powerful Python library designed for symbolic mathematics. Unlike numerical solvers, it performs exact calculations using mathematical symbols, allowing users to model and solve complex problems across various domains. Core capabilities include symbolic calculus (derivatives, integrals, limits), advanced algebra, solving systems of equations, linear algebra (determinants, eigenvalues), and specialized modules for physics and number theory. It can also generate executable code in languages like C and Fortran, making it invaluable for academic research and scientific computing.

The Azure CLI command group for managing Azure Container Registry (ACR). This tool allows users to perform the entire ACR lifecycle, including creating registries, authenticating credentials, building and pushing container images (using cloud builds), importing images from other sources, and configuring advanced features like geo-replication, private endpoints, and webhooks. Essential for CI/CD pipelines and container deployment workflows in Azure.

This skill helps diagnose the health and gather comprehensive status reports for the Ruflo system, including the MCP server and active agents. It provides a read-only workflow to check system status or, when explicitly requested, performs a non-destructive repair and reports the changes.

This skill transforms raw genomic identifiers (VCF, HGVS, rsID) into highly annotated, consequence-predicted records. It utilizes powerful, open-source tools like Ensembl VEP and gnomAD to predict variant consequences (e.g., missense, nonsense), map coordinates, and attach population allele frequencies. Furthermore, it links these molecular findings to clinical and phenotype context extracted by OpenMed, forming a comprehensive research record.

This tool provides a comprehensive, multi-faceted audit of a codebase's health. It scores the project across critical areas like security vulnerabilities, exposed secrets, dependency risks, and structural integrity. Ideal for senior engineers performing pre-commit or pre-release quality assessments to identify weak spots and generate a prioritized remediation plan.

This skill performs an adversarial second pass to scan already de-identified clinical text for residual identifiers (PHI/PII). It combines deterministic regex checks (SSN, phone, dates) with a model-based PII detection rerun. It scores severity and implements a hard block rule: any detected leak prevents data release, ensuring the highest standard of data privacy and compliance.

This skill audits OpenMed NER or de-identification models to detect performance disparities across sensitive demographic subgroups (e.g., sex, age, race). It calculates metrics like per-group recall and leakage disparity, helping users identify instances of 'under-protection' before deployment, ensuring equitable AI governance and compliance.