Skill UI

This skill is designed for understanding complex database structures. It allows users to list all available tables, intelligently filter relevant tables based on natural language queries, retrieve detailed schemas (columns, types, primary/foreign keys), and map inter-table relationships. It culminates in generating the M-Schema format, which is essential for accurately preparing the context needed for advanced SQL query generation.

Datamol is a high-level, Pythonic abstraction layer built over RDKit, designed for computational chemistry and drug discovery. It simplifies complex molecular operations, offering sensible defaults for tasks like SMILES standardization, descriptor calculation, fingerprint generation (ECFP4), 3D conformer analysis, and molecular clustering. It supports efficient parallel processing and cloud storage I/O, making it ideal for handling large-scale chemical datasets.

The HypoGeniC skill facilitates the iterative proposal and scoring of textual hypotheses from labeled datasets using LLMs. It provides tools for generating candidate hypothesis banks and task-prediction statistics. Crucially, it mandates a rigorous, local review workflow, emphasizing dataset auditing (checksums, schemas), configuration validation, and explicitly separating candidate results from definitive scientific conclusions.

Create professional, publication-quality infographics using advanced AI tools. This skill supports multiple infographic types, industry styles, and colorblind-safe palettes. It integrates live web research for accurate data sourcing and employs a smart iterative refinement process (Gemini 3.6 Flash) to ensure the output meets specific professional quality thresholds for marketing, reports, and presentations.

MarkItDown is a lightweight utility designed to convert heterogeneous documents (PDF, Office files, HTML, etc.) into structured, plain Markdown. It is specifically optimized for text analysis, search indexing, and preparing data for Large Language Models (LLM) and Retrieval-Augmented Generation (RAG) systems, prioritizing content structure over visual fidelity. Supports local, stream, and advanced multimodal extraction scenarios.

Open Notebook is a self-hosted, open-source alternative to Google NotebookLM, designed for advanced research and document analysis. It allows users to ingest diverse content (PDFs, videos, web pages) and organize them into private notebooks. Key features include AI-powered summarization, context-aware chatting, vector search, and multi-speaker podcast generation, all while ensuring complete data sovereignty by running entirely on your own infrastructure and supporting multiple AI providers.

The Hugging Face Transformers library is a comprehensive toolkit providing access to thousands of pre-trained models across diverse domains including Natural Language Processing (NLP), Computer Vision, Audio, and Multimodal tasks. It enables developers to easily load models, perform high-level inference (e.g., text generation, classification, QA), and fine-tune models on custom datasets using streamlined APIs like Pipelines and the Trainer class. Ideal for building advanced, state-of-the-art AI applications.

This skill deploys a containerized integration service to programmatically manage and manipulate Lucidchart diagram documents. It handles sophisticated tasks like manipulating shapes, updating connectors, and synchronizing visual collaboration data using the Lucid API. Ideal for integrating automated diagram generation and maintenance into CI/CD pipelines and enterprise workflows.

Provides a comprehensive architectural blueprint for integrating ambient AI solutions (Abridge) into multi-site health systems. It guides the full process from ambient audio capture to structured note generation, covering data flow, component design, FHIR R4 standards, HIPAA compliance, and integration patterns with major EHRs like Epic and Athena.

A comprehensive skill designed for finance-related information retrieval. It provides dual search capabilities: real-time web searching across multiple engines (Jina, DDG, Baidu) for general market knowledge, and Retrieval-Augmented Generation (RAG) for querying local, proprietary document stores (e.g., daily news). Ideal for financial analysts needing both broad market context and specific internal data insights.

This guide provides expert strategies for optimizing GPU inference performance on CoreWeave infrastructure. It covers GPU selection based on workload (LLM, image generation, training), advanced techniques like continuous batching (using vLLM), autoscaling setup (HPA), and benchmarking data. Use this to maximize GPU utilization, minimize latency, and optimize large-scale AI model deployment.

This expert acts as a dedicated data analysis partner for Product Managers. It helps users explore data, write complex queries (e.g., SQL, BigQuery), analyze key metrics, and create structured dashboards. Use this tool when you need to derive actionable insights from complex datasets, compare user cohorts, or understand product performance trends to inform critical business decisions.