Skill UI

A comprehensive coach designed to teach principled negotiation and ethical influence techniques, drawing from frameworks like Harvard's 'Getting to Yes' and Cialdini's psychology. It equips users to handle salary talks, business deals, and cross-cultural disputes by mastering concepts like BATNA, ZOPA, and anchoring. The coach emphasizes ethical practice, focusing on mutual benefit and value creation rather than manipulation.

This skill transforms the AI into a coach for rigorous, systematic thinking. It helps users analyze arguments, detect logical fallacies (like ad hominem or false dilemma), recognize cognitive biases, and evaluate the credibility of evidence and sources. It provides a framework for developing deep reasoning skills necessary for navigating misinformation and complex real-world issues.

Aeon is a comprehensive, scikit-learn compatible Python toolkit designed for advanced time series analysis. It supports a wide range of temporal machine learning tasks, including classification, regression, clustering, forecasting, anomaly detection, and similarity search. Use this skill when working with sequential patterns, time-indexed observations, or complex temporal data that requires specialized algorithms.

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.

DeepChem is a comprehensive Python library designed for applying machine learning to complex fields like chemistry, materials science, and biology. It enables users to perform molecular property prediction (e.g., ADMET, toxicity), train models using specialized graph neural networks (GNNs), and process diverse molecular formats (SMILES, SDF, FASTA). It provides extensive featurization options, making it foundational for drug discovery and computational chemistry research.

Geniml is a comprehensive Python package designed for applying machine learning models to genomic interval data, typically stored in BED files. It provides several unsupervised methods—including Region2Vec and BEDspace—to learn robust embeddings of genomic regions, single cells (scATAC-seq), and associated metadata. Use cases include dimensionality reduction, structural variation analysis, cell-type annotation, and cross-modal querying in genomics research.

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.

Gtars is a high-performance toolkit written in Rust with Python bindings, designed for comprehensive analysis of genomic interval data. It provides specialized modules for overlap detection, generating coverage tracks (WIG/BigWig), tokenization for Machine Learning models, and detailed fragment analysis in single-cell genomics. Use it for robust bioinformatics pipelines and computational genomics research.

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.

Modal is a powerful serverless cloud platform designed specifically for running AI and Machine Learning workloads. It allows users to deploy, serve, and scale complex models (like LLMs) without managing infrastructure. Key features include on-demand access to high-end GPUs (A100, H100), serverless function scaling, persistent storage, and the ability to define entire infrastructure using pure Python code, eliminating the need for separate YAML or Dockerfiles.

Molfeat is a comprehensive Python library designed for molecular featurization. It converts chemical structures (SMILES/RDKit) into numerical feature vectors, supporting over 100 featurizers like ECFP, MACCS, and deep embeddings (ChemBERTa). It unifies featurization pipelines, making it ideal for QSAR modeling, virtual screening, and integrating into deep learning ML workflows.

An expert skill for transforming CPU-bound Python code into highly performant GPU-accelerated code using NVIDIA technologies. It specializes in optimizing scientific workflows, including machine learning (ML), physics simulations, geospatial analysis, and large-scale array computations. Utilize tools like CuPy for array operations, Numba CUDA for custom kernels, and Warp for spatial computing and differentiable simulations, achieving dramatic speedups (10x to 1000x). Use this when dealing with NumPy, pandas, SciPy, or complex computational loops that benefit from parallel processing on NVI...