Skill UI

This coach is designed to systematically develop creative thinking and problem-solving skills. It guides users through established methodologies such as Design Thinking, SCAMPER, TRIZ, and Lateral Thinking. It teaches users how to move beyond initial ideas, overcome creative blocks, and apply structured frameworks to generate, refine, and implement novel solutions across various domains. Ideal for students, professionals, and entrepreneurs facing complex challenges.

This guide teaches advanced search techniques for identifying specific video games. It emphasizes using highly discriminative technical details such as specific platforms (e.g., DOS, NES, PS4), distribution methods (shareware), game engines, era, and detailed review scores from niche sites. By combining multiple constraints (Platform + Era + Detail), users can dramatically narrow down search results, even locating specific developers or artists involved in the game's creation.

This skill provides comprehensive integration with the Benchling cloud platform using the Python SDK and REST API. It allows users to programmatically manage critical life sciences data, including registry entities (DNA/RNA/proteins), inventory tracking, ELN entries, workflows, and querying the Data Warehouse. Ideal for automating complex lab data pipelines and syncing Benchling with external systems.

Biopython is a robust, comprehensive toolkit for computational molecular biology written in Python. It enables users to manipulate biological sequences (DNA, RNA, protein), parse standard formats (FASTA, GenBank, PDB, etc.), and programmatically access major bioinformatics databases like NCBI via Entrez. This skill is ideal for developing complex bioinformatics pipelines, including sequence alignment, structural analysis, and phylogenetic tree construction.

A comprehensive toolkit for developing and executing genomics pipelines on the DNAnexus cloud platform. It provides capabilities for app/applet building, managing large biomedical datasets (FASTQ, BAM, VCF), orchestrating complex workflows, and interacting programmatically using the dxpy Python SDK.

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.

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.

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.

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.

LaminDB is an open-source, lineage-native lakehouse specifically designed for complex biological datasets and models. It provides unified capabilities for querying, tracking data lineage, validating schemas, and ensuring FAIR compliance across diverse data types (e.g., AnnData, Parquet, Zarr). Use this skill when managing multi-modal genomic data, tracking computational workflows (like Nextflow/Snakemake), or building robust, reproducible data infrastructure in bioinformatics.

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.

This skill provides a comprehensive Python API integration for the OMERO platform, an open-source solution for managing and analyzing large-scale microscopy datasets. Users can programmatically access images, retrieve structured data, analyze raw pixel data, manage complex annotations (ROIs), and execute batch processing workflows essential for high-content screening and advanced biological research.