Skill UI

A systematic, non-mutating investigation designed for complex software failures, including bugs, regressions, crashes, and flaky behavior. This skill launches four specialized, read-only sub-agents in parallel to perform deep root-cause analysis, trace code paths, pinpoint the smallest failing seam, and predict the quickest path to confirming or fixing the issue. It is designed purely for diagnosis and hypothesis generation.

A comprehensive, four-phase methodology for debugging complex software issues. It guides users through root cause investigation, pattern analysis, hypothesis testing, and controlled implementation. The core principle emphasizes finding the underlying cause rather than applying quick, symptomatic fixes, ensuring robust and reliable code improvements across various failure scenarios.

This sophisticated skill is designed to locate the root cause of a confirmed software failure. It performs a systematic, semi-formal tracing process: first tracing backward from the observed failure point (using provided stack traces, assertions, or error messages) to identify the original premise, and then tracing forward to confirm the symptom. It outputs a highly structured report detailing the divergence, premises, and remedy.

This skill covers the principles of building Software Development Kits (SDKs) that provide an exceptional developer experience (DX). Learn how to design intuitive APIs, utilize progressive disclosure, establish sensible defaults, and implement actionable, guiding error messages. The focus is on driving product adoption by making the developer's workflow seamless and productive. Ideal for product managers, technical leads, and API designers.

This skill applies rigorous, F1-inspired standards to software quality assurance. It executes comprehensive test plans, classifies failures across five critical tiers (Brake, Engine, Aero, Tyre, Pit), audits Continuous Integration (CI) signal integrity, and generates detailed WJTTC reports. Use it to validate functionality, reproduce complex bugs, or determine the readiness of a release.

WGM is a robust, agentic methodology designed to transform vague or rough requirements into working, validated software. It enforces a disciplined workflow across six stages: deep alignment (Grill), structured planning, deterministic iterative building (Loop), mandatory holdout-scenario judging, and final documentation audits. It ensures that the resulting code converges against rigorously tested acceptance criteria rather than relying on single-pass generation.

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 expert skill guides the creation of commercial-grade, highly optimized, and professional Windows installers for Python applications. It leverages Nuitka for extreme compilation (LTO, slimming, 32/64-bit selection) and Inno Setup for robust packaging. The goal is to deliver the smallest size, fastest startup time, and a residue-free user experience, strictly avoiding basic single-file builds. Ideal for professional software deployment.

This skill provides comprehensive guardrails for handling Protected Health Information (PHI) in US healthcare software. It guides development teams on data classification, security best practices, access control, and audit logging to ensure strict adherence to HIPAA regulations. Use it when dealing with patient data storage, transmission, or processing to prevent breaches and maintain compliance.

Hookify rules enable developers to define custom, event-driven workflows using Markdown files with YAML frontmatter. These rules monitor specific operational events (such as bash commands, file edits, or user prompts) and can trigger warnings or block operations when predefined regex patterns are matched. They are essential for enforcing coding standards, preventing dangerous operations, and guiding development adherence in complex software environments.

A comprehensive, end-to-end methodology designed for software engineers and AI agents to safely and systematically integrate machine learning models into existing non-ML codebases. It guides users through critical phases, including problem framing, data readiness, architectural decoupling (via APIs/services), baseline model implementation, and final MLOps pipeline setup.

Master professional Python testing practices using the pytest framework. This guide covers Test-Driven Development (TDD) methodology, advanced features like fixtures (with different scopes), parametrization, assertion techniques, and achieving high code coverage, ensuring robust and maintainable software.