Skill UI

DeepChem is a Python library for molecular machine learning, providing featurizers, dataset loaders, splitters, and models spanning traditional ML to GNNs for property prediction, drug discovery, and materials design.

TorchDrug is a PyTorch-native toolbox for building graph neural networks tailored to molecules, proteins, and biomedical knowledge graphs, covering property prediction, generation, retrosynthesis, and knowledge graph reasoning with curated datasets and models.

This skill acts as an expert assistant for the Makepad DSL (Domain Specific Language) used with the Rust `makepad-widgets` crate. It helps users generate correct DSL code, explain complex syntax rules (like inheritance and property overriding), and understand how to link standard Rust structures to UI definitions. Ideal for developing complex, live-designed user interfaces within the Makepad framework.

Guides judicial and extrajudicial auction property appraisals with ABNT NBR 14653 principles, market vs forced liquidation values, comparative/rent/cost methods, CUB rates, and risk margins for Brazil.

This skill provides a comprehensive audit framework for analyzing judicial and extrajudicial auction notices in Brazil. It systematically guides users through identifying hidden risks, dangerous clauses, property debts (IPTU, condo fees), occupancy status, and verifying the true valuation, ensuring a safer and more informed bidding process for real estate investors.

Provides senior-level guidance on Brazilian judicial and extrajudicial property auctions, combining legal, appraisal, market, and risk modules to recommend bidding strategies and investor actions.

Audits Brazilian property auctions by scoring 36-point legal, financial, operational risks, running four-scenario stress tests, and weighing ROI by risk to recommend whether to bid.

A comprehensive guide to writing reliable and maintainable tests for Kotlin projects. This skill covers modern testing patterns using Kotest for various spec styles (FunSpec, BehaviorSpec), MockK for dependency mocking, and enforces TDD principles (RED/GREEN/REFACTOR). Learn how to implement unit tests, property-based testing, and ensure high code coverage with Kover.

This tool helps detect Broken Object Property Level Authorization (BOPLA) vulnerabilities, which are critical security flaws detailed in OWASP API3:2023. It scans APIs for excessive data exposure (reading sensitive fields) and mass assignment risks (modifying unintended properties) by simulating malicious requests and analyzing responses. Ideal for security testing, threat hunting, and implementing robust API security controls.

This skill guides security testers in identifying excessive data exposure in REST and GraphQL APIs. It involves intercepting API responses and analyzing them for leaked Personally Identifiable Information (PII), internal identifiers, or sensitive business data that the client UI does not display but the API transmits. It maps directly to OWASP API3:2023 Broken Object Property Level Authorization, helping prevent data leaks via over-fetching.

A comprehensive skill for detecting and exploiting JavaScript prototype pollution vulnerabilities in both client-side and server-side applications. It demonstrates advanced property injection techniques to achieve critical security outcomes, including Cross-Site Scripting (XSS), Remote Code Execution (RCE), and authentication/authorization bypass, especially in Node.js environments and JSON merging scenarios.

A comprehensive guide to writing robust and maintainable tests in Rust. It covers various advanced testing patterns, including Unit, Integration, Async, and Property-Based testing. Learn how to apply the TDD (Test-Driven Development) cycle (RED-GREEN-REFACTOR) to enhance code reliability and ensure high code coverage.