Skill UI

This skill generates a highly customized code review checklist for any pull request. Instead of using a generic template, it adapts the review depth and specific checks based on the target language (e.g., Python, TypeScript), the nature of the change (e.g., bug fix, refactor), and the associated risk level (low, medium, high, critical). It ensures comprehensive coverage, guiding reviewers to focus on critical areas like security, correctness, and architectural integrity.

This tool conducts structured reviews of various Infrastructure-as-Code (IaC) tools (e.g., Terraform, Pulumi) across multiple cloud providers. It generates an actionable report that categorizes findings by severity (Critical, High, Medium, Low), providing detailed remediation steps and a reusable best-practice checklist for continuous security, compliance, and operational auditing.

This skill automates the creation of high-quality, structured pull request descriptions. By analyzing git diffs, commit lists, or raw changes, it generates comprehensive sections covering the summary, motivation, specific changes, detailed testing procedures, and crucial reviewer guidance. Use it to ensure every code change is fully documented for effective peer review.

An adversarial Quality Assurance (QA) pass designed to deep-dive into any codebase for logic errors, security flaws, and functional bugs. Instead of confirming that code 'looks right,' this skill actively attempts to break the code by simulating hostile inputs, tracing critical data paths, and checking for subtle, real-world correctness issues. Essential for high-stakes, critical code reviews.

A comprehensive skill for triaging npm packages and dependency lockfiles against supply-chain threats. It utilizes heuristic scanning (GuardDog), manual tarball inspection, and dynamic detonation in isolated environments to detect malicious behavior such as credential exfiltration, reverse shell execution, and self-propagation. Essential for secure code review and CI/CD pipeline gating.

This skill provides expert guidance on design engineering, focusing on UI polish, micro-interactions, and high-craft component decisions. It is ideal for reviewing frontend code (React, CSS, Tailwind) to elevate perceived quality, addressing details like easing, duration, and physical feedback. Use it when subtle detail and refined taste are critical differentiators.

This specialized skill reviews user interface animations, motion patterns, and interactive code (CSS, Framer Motion, etc.) against a strict, high-craft standard. It evaluates not only functionality but also performance, accessibility, physical correctness, and adherence to motion design principles. Use it when reviewing diffs related to transitions, keyframes, or gestures to ensure the motion feels right, not just that it runs.

This skill structures a highly controlled, bounded development life cycle for AI agents. It mandates a structured three-phase workflow: Planning (Spec), Implementation (Build), and Verification (Review). It is essential for building complex features from scratch, especially when dealing with ambiguous requirements, high risk, or needing explicit human approval gates to ensure the resulting code meets all defined specifications.

This comprehensive tool performs an architectural audit of a codebase, mapping module dependencies, checking layering integrity, and flagging structural decay. Drawing on classic engineering principles (like Conway's Law and Testability Seams), it provides a deep assessment of the project's health and structure. Ideal for large-scale code reviews or onboarding new developers.

This skill provides comprehensive pull request (PR) code reviews that go beyond simple syntax checks. It aggressively surfaces deep-rooted decay risks, architectural design smells, and maintainability issues. Findings are reported using a structured Symptom → Source → Consequence → Remedy framework, drawing insights from classic engineering principles. Ideal for reviewing complex or crucial code modules.

This skill provides a comprehensive test quality review, drawing on established principles from classic engineering texts like xUnit Test Patterns and How Google Tests Software. It diagnoses structural flaws—such as brittleness, decay, and poor coverage—in existing test suites, helping ensure that the codebase and its tests are robust, maintainable, and adhere to high industry standards.

A comprehensive guide for developers building AI demos and GPU compute tasks on Hugging Face Spaces using ZeroGPU hardware. It covers best practices for writing and reviewing code that utilizes the `@spaces.GPU` decorator, managing runtime duration and quota, and understanding the specific CUDA availability model and process isolation rules required for reliable ML deployment.