Skill UI

Methodology for assessing WPS-enabled routers using Pixie Dust offline recovery, online PIN brute force, lockout handling, and PBC edge cases. Useful for wireless security testing and understanding WPS exposure on consumer APs.

Invariant-Guard enforces a correctness-first approach to algorithm design. Instead of writing code and hoping it works, this skill mandates defining the function contract, loop invariants, termination arguments, and all potential edge cases *before* implementation. It is essential for complex algorithms where subtle bugs (like off-by-one errors or missed post-conditions) are common, ensuring mathematically rigorous and verifiable code.

Quinn specializes in writing and executing comprehensive test suites to rigorously prove system functionality. This role goes beyond simple pass/fail checks, focusing on mapping acceptance criteria, identifying functional gaps, and covering critical edge cases (e.g., nulls, boundary values, error paths). It validates the system's reliability across Unit, Integration, and End-to-End layers.

Generates comprehensive and structured developer handoff annotations for any Figma screen or component. This tool transforms raw visual designs into precise, buildable specifications, covering interactions, states (loading, error, success), accessibility requirements, and edge cases, ensuring developers receive everything needed for implementation.

Generates championship-grade, F1-inspired test suites based on the WJTTC methodology. This skill enforces a 'tests first' philosophy, analyzing the codebase to define structured test plans. It covers standard industry unit tests (Layer 1) and deep expert edge cases (Layer 2). Furthermore, it includes Signal Integrity Audits to ensure the test suite's reliability is paramount, guiding development toward maximum code quality.

This comprehensive guide outlines a rigorous, human-in-the-loop engineering workflow for leveraging advanced AI agents to complete complex implementation tasks. It mandates defining clear completion criteria, decomposing work into manageable units, and strategically routing models (Haiku, Sonnet, Opus) based on task complexity. The workflow emphasizes continuous evaluation, regression testing, security review of invariants and edge cases, and strict cost discipline to ensure robust and reliable software development using AI assistance.

This skill executes the final two phases (Refinement and Completion) of the structured SPARC development methodology. It systematically elevates an implemented feature to production-grade quality by performing detailed code reviews (verifying compliance against specifications, architecture, and quality standards), maximizing test coverage (unit, integration, and edge cases), validating performance, and generating comprehensive documentation and traceability matrices. It ensures the feature is fully ready for deployment.

This tool executes the initial SPARC (Systematic Project Architecture and Requirements Collection) phase. It guides users through defining comprehensive functional and non-functional requirements, setting measurable acceptance criteria (Given/When/Then), and identifying critical constraints and edge cases. Use this when starting a new project to establish a solid, foundational specification before development begins.