Skill UI

This skill allows users to generate and edit high-quality, general-purpose images using advanced AI models like FLUX.2 and Gemini. It is suitable for creating photorealistic images, artistic illustrations, concept art, marketing visuals, and general visual assets. Note that this tool is designed for creative content, not technical schematics or diagrams.

This skill provides comprehensive tools to analyze and engineer protein glycosylation patterns. It scans amino acid sequences for canonical N-glycosylation sequons (N-X-S/T) and predicts O-glycosylation hotspots. Use cases include optimizing therapeutic antibodies, designing vaccine antigens, and characterizing biosimilars by rationally modifying glycan structures for improved stability, function, or immune evasion.

Hypogenic is an advanced framework that uses Large Language Models (LLMs) to systematically generate and test hypotheses on complex tabular datasets. It supports three powerful modes: data-driven generation (HypoGeniC), literature-grounded refinement (HypoRefine), and combined union methods. This tool is essential for accelerating scientific discovery in fields such as predictive modeling, content analysis, and deception detection by combining empirical data insights with theoretical knowledge.

A comprehensive toolkit for medical device manufacturers seeking ISO 13485 certification. This skill guides users through gap analysis, creating mandatory Quality Management System (QMS) documentation, and ensuring compliance with global medical device regulations (e.g., EU MDR, FDA QMSR). It provides templates, guidance, and structured processes for foundational QMS documents like Quality Manuals and CAPA procedures.

Use terminal commands to discover, list, create, edit, move, copy, enable/disable, and delete AI agent skills across 11 tools, handling format conversions and scope overrides between global and project configurations.

PathML is a comprehensive Python framework designed for computational pathology workflows. It supports end-to-end analysis of Whole Slide Images (WSI), including loading 160+ proprietary formats, performing advanced stain normalization and tissue segmentation, constructing spatial cell graphs, and training deep learning models (e.g., HoVer-Net) for multiplex immunofluorescence and spatial proteomics. Ideal for large-scale biomedical image analysis.

This skill provides programmatic access to the PrimeKG, a comprehensive knowledge graph integrating over 20 primary databases. It facilitates multiscale biological research by mapping complex relationships between genes, drugs, diseases, and phenotypes. Use it for drug repurposing, identifying novel disease targets, and analyzing molecular mechanisms across various biological scales.

A comprehensive Python library for handling DICOM (Digital Imaging and Communications in Medicine) files. Use this skill for reading, writing, and manipulating complex medical images such as CT, MRI, and X-ray. Features include metadata extraction, pixel data manipulation, image anonymization (PHI removal), multi-format conversion, and processing multi-slice volumes, making it essential for medical data analysis and PACS integration.

PyHealth is a comprehensive Python toolkit designed for building deep-learning pipelines in the clinical and healthcare domains. It supports diverse data modalities, including EHR records, physiological signals, and medical images. The framework follows a stable 5-stage pipeline (Dataset → Task → Model → Trainer → Metrics), enabling users to perform complex tasks like predicting mortality, optimizing drug recommendations, staging sleep, or cross-mapping medical codes (ICD/ATC/RxNorm) on datasets like MIMIC-III/IV and OMOP.

PyTDC (Therapeutics Data Commons) is an open-science platform providing comprehensive, AI-ready datasets and benchmarks for the entire drug discovery and development pipeline. It supports single-instance prediction (e.g., ADME, toxicity), multi-instance prediction (DTI, DDI, PPI), and novel molecule generation (MolGen). Use this skill for benchmarking machine learning models and predicting complex biomedical properties.

A comprehensive guide for diagnosing production performance issues in Qdrant vector databases. Learn how to systematically check the optimizer status, analyze high memory usage (OOM crashes), and troubleshoot slow query latencies using key metrics and dedicated API endpoints. Essential for maintaining optimal operational health.

This skill provides comprehensive, real-time research lookup by intelligently routing queries to the optimal backend. It utilizes fast web search for general market data, advanced APIs for deep synthesis research, and dedicated services for academic papers and DOI searches. Use it for literature verification, background context gathering, and technical documentation retrieval.