Skill UI

This comprehensive guide provides a detailed workflow for parsing Windows LNK shortcut files in a digital forensics context. It extracts critical artifacts including target paths, timestamps (creation, access, modification), volume serial numbers, and machine identifiers. It is essential for reconstructing user file access history, tracking accessed files on network shares, and building robust forensic timelines.

ADHX provides a dedicated API to fetch X/Twitter posts as clean, structured JSON. It extracts comprehensive data, including author profiles, engagement metrics (likes, retweets), and full article content for long-form posts. This method is vastly superior to traditional scraping techniques, making it ideal for LLM analysis, summarization, and structured data workflows.

A comprehensive solution for systematically auditing dataset health. It profiles data distributions, detects missing values (MCAR/MAR/MNAR), identifies outliers using multiple methods (IQR, Z-score), and calculates a Data Quality Score (DQS). It provides actionable remediation plans and supports monitoring setup for live pipelines.

This framework manages the ingestion, organization, synchronization, and search of knowledge across multiple storage layers, including local files, persistent memory, vector stores, and Git repositories. It provides a structured approach for ensuring knowledge durability, deduplication, and maintaining a 'single source of truth' across complex development and operational contexts.

deepTools is a comprehensive suite of command-line tools for analyzing high-throughput sequencing data. It supports quality control (QC), normalization, sample comparison, and generating publication-quality visualizations for various experiments, including ChIP-seq, RNA-seq, and ATAC-seq. Key functions include converting BAM to bigWig, calculating correlations, and generating heatmaps/profiles around genomic features.

FlowIO is a specialized Python library for reading, writing, and managing Flow Cytometry Standard (FCS) files. It efficiently parses complex FCS metadata, extracts raw event data into NumPy arrays, and supports handling various channel types (scatter, fluorescence, time). It is indispensable for integrating flow cytometry data into data pipelines and performing initial preprocessing steps.

Geniml is a comprehensive Python package designed for applying machine learning techniques to genomic interval data (BED files). It provides advanced, unsupervised methods for learning high-dimensional embeddings of genomic regions, single cells (scATAC-seq), and metadata. Core functionalities include Region2Vec for region embeddings, BEDspace for joint region-label analysis, scEmbed for single-cell data, and utilities for building consensus peak universes, enabling robust clustering and feature extraction in genomics research.

Gtars is a comprehensive, high-performance toolkit written in Rust, featuring Python bindings for advanced genomic interval analysis. It is designed for computational biology and machine learning applications, providing specialized modules for overlap detection (IGD), coverage track generation (WIG/BigWig), genomic tokenization, and single-cell fragment analysis. Use it for processing BED files, validating reference sequences, and building robust bioinformatic pipelines.

A powerful Python library for reading, writing, and manipulating DICOM files, the standard format for medical imaging data. Use it for extracting pixel data from various scans (CT, MRI, X-ray), managing complex metadata tags, handling data compression, performing patient data anonymization, and converting medical images to standard formats for research and clinical applications.

Pysam is a powerful Python module designed for reading, manipulating, and writing core genomic file formats. It provides a Pythonic interface to htslib, enabling users to process alignment files (BAM/CRAM), variant calls (VCF/BCF), and reference sequences (FASTA/FASTQ). Use it for implementing complex bioinformatics pipelines, calculating coverage, quality control, and detailed variant annotation.

Provides quick, LLM-optimized summaries for a single academic paper using arXiv IDs or URLs. This skill employs a tiered source fallback system, starting with a structured overview, progressing to full markdown, and finally inspecting the source LaTeX files for deep details. It is designed for rapid comprehension of one specific paper, not for broad literature reviews.

Use the `idc-index` library to query and download vast public cancer imaging datasets from the National Cancer Institute Imaging Data Commons (IDC). This skill allows users to filter metadata, visualize images, and download DICOM files for large-scale AI model training and deep medical research. No authentication is required for data access.