Skill UI

This advanced tool utilizes the Digital Human DNA (DHDNA) framework to extract a comprehensive cognitive signature from any piece of text. It analyzes the author's unique thinking patterns, reasoning methods, decision-making styles, and core values across 12 detailed dimensions (e.g., Analytical Depth, Ethical Reasoning, Emotional Processing). It provides a structured profile, revealing the topology of the mind behind the writing, without judgment.

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.

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.

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.

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.

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.

Latch is a robust Python framework designed for building, deploying, and managing complex, scalable bioinformatics workflows as serverless pipelines. It supports integrating multiple analysis tools (like Nextflow/Snakemake) and handles cloud data management (LatchFile/LatchDir) and detailed resource configuration (GPU, CPU, memory). Ideal for running large-scale analyses such as RNA-seq, AlphaFold, and single-cell analysis in a reproducible, production-ready environment.

MarkItDown is a powerful tool designed to convert a wide array of file types—including PDF, DOCX, images, audio, and structured data (JSON/XML)—into clean, token-efficient Markdown format. It supports advanced features like OCR for scanned documents, transcription for audio, and AI-enhanced descriptions, making diverse content readily consumable and structured for modern Language Models and AI pipelines.

Matchms is an open-source Python library designed for comprehensive mass spectrometry data processing. It handles spectrum comparison, calculates various similarity scores (like cosine and modified cosine), and aids in identifying unknown compounds. It is highly valuable for metabolomics and spectral library searching pipelines.

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.

Medchem is a powerful Python library designed for molecular filtering and prioritization in drug discovery. It allows users to apply rigorous, literature-derived drug-likeness rules (e.g., Lipinski, Veber, CNS), detect structural alerts (PAINS, NIBR), calculate complexity metrics, and combine multiple criteria using a custom query language. It is essential for compound library triage and hit-to-lead optimization.