Skill UI

This guide provides a strategic framework for drafting author responses to challenging peer reviews in academic conferences. It teaches authors how to structure replies by addressing four key axes (Correctness, Novelty, Backing, Clarity), ensuring technical precision, maintaining anonymity, and framing commitments that survive the subsequent Area Chair (AC) discussion phase.

This tool provides a preferred, end-to-end method for modifying code in real-world repositories. It simulates a professional development workflow: cloning the target repository, running changes on a dedicated task branch, and critically, verifying the implementation through an independent red $\to$ green $\to$ regression test loop. It is designed for fixing GitHub issues, implementing new features, or adding functions to existing codebases, ensuring the structural integrity and correctness of the changes.

An advanced tool designed to audit, validate, and maintain rule files (e.g., .claude/rules/). It checks structural correctness, validates glob patterns against actual project files, and runs an interactive session to confirm or improve the relevance of each rule. Ideal for initial setup, debugging, or periodic rule hygiene.

This tool leverages an LLM-as-a-Judge agent to thoroughly evaluate staged Git changes before they are committed. It checks for correctness, completeness, and safety issues, providing a detailed verdict (APPROVE, NEEDS_REVIEW, REJECT) and actionable feedback. It is essential for maintaining high code quality, especially in complex or security-sensitive code sections.

A comprehensive, stack-agnostic tool for deeply evaluating agent harnesses. It checks for broken code paths, redundant instructions, and the overall usefulness of agent logic using a structured, multi-stage dual-judge protocol. The process involves high-priority questionnaires (Q1, Q2) before running three defined tracks: A (Correctness), B (Redundancy), and C (Usefulness). Ideal for auditing complex agent systems before deployment or major refactoring.

Reviews a pull request and posts one consolidated GitHub review with inline comments via the gh CLI. Runs the repo's own checks first, verifies external claims against official docs, then reports correctness, security, and structural findings—each backed by evidence and gated for noise.

This skill verifies an MCP server before release by exercising a real protocol session. It compares runtime capabilities with source code and documentation, tests failure paths, and records reproducible evidence. Use this when shipping or reviewing an MCP server, tool, resource, prompt, catalog, or install path to ensure protocol behavior and transport correctness.

This skill provides a comprehensive framework for performing end-to-end UI testing and verification on Android emulators. It guides users through systematic processes including device calibration, UI element discovery using uiautomator, performing complex interactions (tap, swipe, input), and robust reporting via visual screenshots and logcat analysis. Ideal for ensuring the stability and functional correctness of React Native or native Android applications.

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.

This skill helps transform code reviews from simple gatekeeping into a powerful knowledge-sharing mechanism. It guides users through systematic analysis of code changes, focusing on correctness, security, performance, and maintainability. Use it when reviewing pull requests, establishing best practices, or mentoring developers to ensure high-quality, robust codebase development.

This expert AI code reviewer evaluates code across correctness, security, performance, design, and readability. It identifies bugs, vulnerabilities, and anti-patterns with actionable fix suggestions. Supports Python, JavaScript, Go, and Rust. Provides structured feedback with metrics and positive notes to improve code quality.

Validates Conductor workflow artifacts such as index, tracks, plans and status markers for completeness, consistency and correctness. Use after setup, before resuming a track, or when diagnosing stale or conflicting project state.