Skill UI

This skill provides comprehensive tools and methodologies for working with the Brain Imaging Data Structure (BIDS) standard. It is essential for organizing, validating, querying, and converting complex multi-modal neuroscience and biomedical datasets (including MRI, EEG, MEG, PET, etc.). Use this when setting up BIDS-compliant directory structures, ensuring data integrity before publication, or converting raw DICOM scans into standardized formats.

A powerful tool for deterministic and reproducible retrieval of evidence from specialized academic and policy corpora. It supports searching arXiv (preprints), PubMed/PMC (biomedical literature), and US policy documents. Users can check corpus freshness, retrieve stable metadata, and fetch full-document Markdown for high-reliability research tasks.

A comprehensive tool for performing pathway and gene-set enrichment analysis (GSEA, ORA, ssGSEA, GSVA). Used in computational biology to determine which biological pathways or gene sets are significantly over-represented in a given list of genes or ranked gene data. Supports multiple databases (GO, KEGG, Reactome, MSigDB) and provides tools for result interpretation, visualization, and publication-ready reporting.

Use the HasData CLI to access real-time, structured web data from major platforms. This tool provides dedicated subcommands for various APIs (including Google SERP, Amazon, Zillow, Booking, etc.), enabling users to automate data collection, scrape specific URLs, and retrieve structured information like local business listings, product pricing, and real estate data directly from the command line.

A powerful command-line utility that transforms virtually any website into a structured CLI command. By utilizing OpenClaw's browser, it allows users to scrape and extract structured data (such as JSON) from over 36 platforms—including social media, finance, and developer communities—directly from the terminal. This tool eliminates the need for complex scraping code or browser extensions, making web data accessible via simple one-liners.

This tool reads a CSV file containing experimental results (x, y_baseline, y_ours). It utilizes matplotlib to generate a two-line comparative plot of these data points and outputs the visualization directly as a PDF file. It is highly useful for academic purposes, allowing users to easily compare baseline model performance against proposed methods for inclusion in research papers.

Provides a structured, actionable review of Power BI reports across their entire lifecycle—from development to active use. The tool assesses report effectiveness, usage adoption, data performance (load times), and structural integrity. It generates a prioritized list of findings and concrete recommendations, helping users determine if reports are worth maintaining, optimizing, or retiring.

A comprehensive guide and toolset for managing the entire lifecycle of semantic models (Power BI/Analysis Services). Use this skill for designing star schemas, building measures, optimizing performance, setting up incremental refreshes, and auditing models against best practices, all driven through the terminal CLI.

An interactive workflow designed to audit and standardize naming conventions within Power BI semantic models stored as TMDL files. It ensures that tables, columns, measures, and display folders adhere to consistent, human-readable, and business-aligned standards. The tool guides users through gathering critical business context before auditing and applying complex, cross-file reference updates to maintain data integrity.

This tool guides users through the critical process of designing experiments *before* data collection. It helps select appropriate structures—such as factorial, crossover, or blocked designs—to ensure that collected data is interpretable and allows for robust causal inference. Use it when planning scientific studies, assigning subjects, or optimizing responses across multiple factors.

KAPE is a powerful, Windows-native triage tool used for Digital Forensics and Incident Response (DFIR). It executes in two phases: 'Targets' collect raw forensic artifacts (like registry hives, event logs, and prefetch files) from live systems, while 'Modules' parse this collected data into normalized, analyst-ready CSV/JSON formats. It enables fast, defensible, and scalable artifact collection, especially when full disk imaging is impractical.

A static analysis tool that audits SQL code for common cost and performance anti-patterns (e.g., full-table scans, Cartesian joins). It calculates a query health score (0-100) for data warehouses like BigQuery, Snowflake, and Redshift, providing a prioritized, actionable plan with concrete fixes and estimated cost savings. Essential for FinOps and data quality assurance.