Skill UI

This guide provides a comprehensive methodology for load testing and capacity planning within Snowflake data warehouses. It covers optimizing query performance by adjusting warehouse sizes (Scale UP), simulating high concurrency using multi-threading (Scale OUT), and configuring advanced scaling policies (Standard, Economy, Maximized). Users can benchmark critical queries using SQL and simulate real-world user traffic using Python, ensuring robust and cost-efficient production environments.

Test-Driven Development (TDD) is a development methodology that emphasizes writing failing tests first, followed by writing minimal code to pass them (Red -> Green), and finally refactoring the code (Refactor). This approach ensures that code is always verified against its intended public behavior, making the system robust, maintainable, and resilient to internal changes. It teaches developers to focus on 'what' the system does, rather than 'how' it does it.

This skill takes large plans, specifications, or Product Requirement Documents (PRDs) and systematically breaks them down into small, independently verifiable, and actionable GitHub issues. Utilizing the 'vertical slice' (tracer bullet) methodology, it ensures that each generated task cuts through all integration layers (UI, API, Schema, Tests) end-to-end. It manages dependencies, identifies required human interaction (HITL) versus autonomous implementation (AFK), and prepares a structured, executable backlog ready for immediate ticketing.

This guide details a systematic methodology for identifying specific music tracks. Since searching solely by song title is often too generic, users must first establish constraints on the artist (e.g., nationality, birth year, genre, career type) to narrow the search space. Once the artist is identified, the search should focus on their discography, album tracklists, or specific release years to pinpoint the desired song.

A systematic, multi-phase guide for tackling hard bugs and performance regressions. It mandates building a robust feedback loop (e.g., failing tests, harnesses), formulating falsifiable hypotheses, targeted instrumentation, and rigorous regression testing. This methodology ensures that debugging efforts are structured, efficient, and highly reproducible, making it ideal for complex software defects.

Methodology for red team testing of on-prem Active Directory, hybrid AD/Entra ID, and ADCS. Covers reconnaissance, credential abuse, privilege escalation, lateral movement, persistence, trust abuse, and Defender for Identity evasion.

Covers Bluetooth Low Energy attack workflows, including GATT enumeration, unauthenticated characteristic access, pairing downgrade checks, MITM during pairing, sniffing, and companion app reverse engineering for IoT and consumer BLE devices.

Methodology for assessing Bluetooth Classic BR/EDR targets, including device discovery, SDP service enumeration, profile abuse, legacy pairing weaknesses like KNOB, and BlueBorne-style flaws in older embedded, automotive, and accessory devices.

Offensive cloud security methodology for AWS, Azure, and GCP. Covers identity discovery, credential harvesting, enumeration, privilege escalation, persistence, data exfiltration, lateral movement, serverless abuse, Kubernetes paths, and detection evasion during authorized assessments.

A hands-on methodology for testing IoT and embedded devices, covering hardware reconnaissance, firmware extraction and analysis, bootloader access, runtime exploitation, wireless protocol attacks, companion app review, and cloud API abuse.

Methodology for capturing, analyzing, and replaying LoRaWAN and sub-GHz wireless traffic. Useful for testing IoT deployments, fixed-code remotes, garage doors, TPMS, and other devices in 433/868/915 MHz bands.

A practical offensive methodology for Android and iOS app testing. It covers static analysis, Frida/Objection instrumentation, SSL pinning bypass, exported component abuse, insecure storage review, and mobile API reconnaissance for pentest and bug bounty work.