Skill UI

A comprehensive evaluation suite designed to benchmark the performance of Large Language Models (LLM) and Vision-Language Models (VLM) specifically for home security applications. It covers 143 diverse tests across 16 critical domains, including tool use, prompt injection resistance, contextual reasoning, alert triage, and detailed scene analysis, ensuring robustness for smart home AI systems.

This skill analyzes an existing codebase using domain context and architectural decisions to identify architectural friction and "deepening opportunities." It proposes refactored improvements to increase module depth, enhancing testability, improving maintainer locality, and making the codebase more navigable for AI tools. It focuses on transforming shallow modules into robust, deeply leveraged components.

This skill helps users create specialized 'steering documents' or project memory for highly specific domains (e.g., API standards, testing strategies, security patterns). By analyzing the existing codebase and following predefined architectural principles, it synthesizes concise, actionable guides that capture technical patterns, best practices, and domain-specific rules, ensuring project consistency and knowledge retention.

Conduct comprehensive, systematic literature reviews across biomedical, scientific, and technical domains. This skill facilitates meta-analyses, scoping reviews, and research syntheses by searching multiple academic databases (e.g., PubMed, arXiv). It generates professionally formatted reports (Markdown/PDF) with verified citations and mandates the inclusion of AI-generated scientific schematics (e.g., PRISMA flow diagrams). Ideal for academic research and thesis writing.

This skill runs a comprehensive, sequenced pipeline (Patterns 1-5) to structurally analyze a monolithic codebase. It inventories components, detects domain duplication, resolves hierarchy issues, measures inter-module coupling, and groups components into candidate, domain-aligned services. It is essential for architects planning service extraction, providing actionable metrics and DDD insights before refactoring or splitting the application.

A comprehensive methodology for offensive security, red team intelligence gathering, and bug bounty reconnaissance. Covers domain recon, email harvesting, social media profiling, code leaks, infrastructure mapping, cryptocurrency tracing, and geospatial intelligence. Essential for building detailed attack-surface maps against organizations or individuals.

A comprehensive checklist and methodology for testing Cross-Site Scripting (XSS) vulnerabilities. Covers all major types, including Stored, Reflected, DOM, and Blind XSS, along with advanced techniques like CSP bypass, polyglot payloads, and event handler injection. Essential for web application security testing and bug bounty hunting to identify and mitigate malicious script injection risks.

This skill provides comprehensive functionality for provisioning, managing, and scaling Oracle Cloud Infrastructure (OCI) compute instances using the Python SDK. It addresses common challenges like "out of host capacity" errors by implementing robust retry logic across multiple Availability Domains (ADs). Users can select optimal Flex shapes (AMD, ARM, Intel), manage the entire instance lifecycle (start, stop, reboot, terminate), and retrieve network details, ensuring reliable cloud workload deployment.

This comprehensive runbook provides a self-service solution for managing Oracle Cloud Infrastructure (OCI) outages when official status pages are misleading or silent. It guides users through independent service health probes (Identity, Compute, Networking), automated instance recovery actions (reboot, softreset), and critical procedures for cross-Availability Domain (AD) or cross-region failover using boot volume backups to ensure business continuity.

This tool conducts comprehensive searches across major patent databases (like Google Patents and Espacenet) and academic literature (Google Scholar, arXiv) to identify 'prior art' related to a proposed invention. It systematically extracts core concepts, technical features, and relevant classification codes (IPC/CPC). Users can assess the novelty, inventive step, and technical landscape of their ideas, and perform a preliminary Freedom-to-Operate (FTO) assessment before filing or developing a product.

This skill facilitates an interactive Quality Assurance (QA) session. Users can conversationally describe encountered bugs or issues. The agent clarifies the problem, explores the codebase for context and domain language, and then meticulously files durable GitHub issues. It supports both single-issue reports and breaking down complex problems into multiple, independently verifiable tickets, ensuring developers receive actionable, high-quality reports written from the user's perspective.

This comprehensive skill guides users through the entire lifecycle of developing AI agent skills. From initial concept (Understand) to structured design, writing the core skill logic, rigorous testing, and final performance optimization. It ensures skills are robust, accurate, and highly triggerable across any domain, including engineering, content creation, and research.