Skill UI

Dask is a powerful Python library designed for parallel and distributed computing. It allows users to scale existing pandas and NumPy workflows beyond the limits of available RAM, processing massive datasets up to the terabyte scale. It provides specialized APIs (DataFrames, Arrays, Bags) for handling structured, scientific, and unstructured data efficiently across single machines or multi-node clusters.

This skill performs comprehensive Exploratory Data Analysis (EDA) on specialized scientific datasets. It supports over 200 file formats across diverse domains, including chemistry (PDB), bioinformatics (FASTA, BAM), microscopy (TIFF), and spectroscopy. The system automatically detects the file type, assesses data quality and integrity, and generates detailed, format-specific markdown reports, providing deep insights and recommending downstream analyses.

A comprehensive toolkit for advanced geospatial analysis. It covers remote sensing (Sentinel, Landsat), GIS operations (vector/raster/point cloud), spatial statistics, and Machine Learning for Earth Observation. Supports cloud-native workflows, multiple data types, and 8 programming languages, making it ideal for complex scientific research and commercial spatial projects.

This skill proactively detects and reports comprehensive system resources, including CPU cores, GPU types (NVIDIA, AMD, Apple Silicon), available memory, and disk space. It generates a detailed JSON report with strategic, context-aware recommendations for optimizing scientific computing workflows, such as recommending parallel processing libraries (Dask, joblib), out-of-core methods (Zarr), or appropriate GPU acceleration backends (Metal, ROCm). Use this before any large-scale data analysis, model training, or computationally intensive simulation to make informed architectural decisions.

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 systematic tool for scientific inquiry, guiding users through the entire research process. It helps formulate testable, mechanistic hypotheses from raw observations or preliminary data, following the established scientific method. The workflow includes comprehensive literature review, evidence synthesis, generating competing explanations, and designing experiments, all while mandating the creation of scientific schematics for visualization.

This skill provides comprehensive integration with the LabArchives Electronic Lab Notebook (ELN) platform via its REST API. It allows users to programmatically manage notebooks, retrieve user data, automate entry creation, upload diverse attachments (PDF, CSV, images), perform full notebook backups, and generate detailed usage reports. Ideal for automating scientific workflows and integrating ELN data with tools like Jupyter or REDCap.

This advanced skill generates comprehensive, professional-grade market research reports (50+ pages) modeled after top consulting firm deliverables (McKinsey, BCG). It supports deep data analysis using research-lookup, multi-framework strategic assessment (PESTLE, SWOT, Porter's Five Forces), and extensive visual generation using scientific schematics and image generation tools. The output is in professional LaTeX format, compiled to PDF, suitable for strategic planning, investment decisions, and thought leadership.

A comprehensive guide to MATLAB and GNU Octave, covering core scientific computing tasks. This skill details capabilities in matrix operations, linear algebra, data visualization (2D/3D), statistical analysis, signal processing, and solving differential equations. It is ideal for users requiring complex numerical simulation, data modeling, and scientific scripting.

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 systematic framework designed for conducting rigorous peer reviews of scientific manuscripts and grant proposals. It provides a structured, checklist-based evaluation covering methodology rigor, statistical validity, reporting compliance (e.g., CONSORT, STROBE), and assesses all sections from abstract to results. Ideal for academic publishing, research quality assessment, and providing detailed, constructive feedback.

This skill provides a systematic framework for evaluating the rigor of scientific claims, research methodology, and evidence quality. It guides users through assessing experimental design validity, identifying various types of biases (e.g., selection, cognitive), and applying established grading frameworks like GRADE or Cochrane Risk of Bias. Ideal for systematic reviews, peer review, and critical analysis of scientific literature.