Skill UI

This specialized skill is designed for advanced security analysis, tracing the complete trust chain from cloud metadata services to accepted privilege. It is used in penetration testing or CTF environments to meticulously prove how metadata-derived credentials (such as instance identities or tokens) are issued and subsequently accepted by a cloud or control plane API, thereby exposing the decisive trust edge.

A comprehensive guide for conducting authorized database security assessments across various platforms (PostgreSQL, MySQL, MSSQL, Mongo, Redis). Covers critical areas including exposure analysis, authentication, authorization checks, misconfiguration review, and attack chain validation, ensuring non-destructive testing practices are followed.

This skill automatically detects and extracts tabular laboratory panels from diverse sources, including scanned images, photos, and delimited files (CSV/TSV). It converts complex visual data into clean, structured rows, capturing the test name, measured value, unit, reference range, and abnormal flag. It is essential for preparing medical data for subsequent standardization using LOINC and UCUM, and for mapping into FHIR resources.

Synthea generates statistically realistic, fully synthetic patient records in multiple standards (FHIR R4, C-CDA, CSV). This tool is essential for developers and researchers needing safe, shareable test data for OpenMed pipelines, CI fixtures, or specialized de-identification leakage gate test sets. It ensures zero risk of exposing real Protected Health Information (PHI).

Automates the creation of isolated Git worktrees for safe and efficient feature development. This process handles branch validation, optimizes dependency installation (e.g., symlinking node_modules), runs background verification tests, and provides guidance for database isolation, ensuring developers always work in a clean, separated environment.

This skill enables the execution of OpenSquilla agents against complex, real-world software engineering benchmarks (SWE-bench). It runs the entire process—from pulling the necessary Docker image to collecting the patch and running official evaluations—in a containerized environment. Use it to test an agent's ability to solve specific coding issues, benchmark performance across datasets (e.g., verified, multilingual), or verify if a generated patch successfully resolves an instance.

This skill manages the complex, multi-step workflow for updating the company's threat intelligence database. It delegates the core research and content updates to the dedicated AgentSec system. The process involves reading AgentSec's source material, executing rigorous testing in an isolated workspace, synchronizing the validated security feed, and finally ensuring data integrity by validating the updates across the guide and landing mirrors.

This guide outlines a structured methodology for authorized wireless security assessments. It covers essential techniques such as Wi-Fi data capture, WPA/WPA2 handshake analysis, and rogue Access Point detection. It is designed for assessing corporate network security, verifying wireless isolation, and providing professional penetration testing recommendations, strictly adhering to legal scope and authorization.

A systematic methodology for resolving discrepancies between conceptual models (documentation, tests, diagrams, metrics) and actual observed behavior (live code execution or real data). This process mandates that observed reality (the 'territory') always overrules the theoretical map, ensuring system integrity and accurate knowledge updates.

This skill guides users through a comprehensive Legitimate Interest Assessment (LIA) required under GDPR Article 6(1)(f). Utilizing the official EDPB three-step test, it helps users systematically determine if processing personal data based on legitimate interest is legal, proportionate, and necessary. It is crucial for documenting compliance records, especially concerning AI training, web scraping, and direct marketing.

This tool provides a statistically rigorous framework for analyzing a user's private LinkedIn post performance data. It moves beyond simple averages, utilizing median/MAD and permutation testing to determine if observed patterns are genuine or statistical noise. It guides users through three steps: descriptive analysis, pattern mining, and sizing future A/B experiments, ensuring hypotheses are testable and avoiding over-conclusion. Works only with your own exported data.

This tool allows users to retrieve the complete, up-to-date metadata and audio URL for a specific music production ID. It is essential for checking the current processing status, such as whether stems, MIDI, or mastering have been successfully generated, ensuring the user has the latest information before proceeding to the next workflow step.