Skill UI

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 skill validates the completeness, consistency, and correctness of Conductor project artifacts. It checks for the existence of required directories, verifies essential project documentation (like index.md, product.md, etc.), and confirms adherence to established naming and status patterns. Use this tool after setup, when diagnosing issues, or before implementation to ensure the project context is sound.

A comprehensive expert for configuration management, specializing in validating, testing, and ensuring the correctness of application settings. It creates robust validation schemas (e.g., JSON Schema) and performs critical checks, including identifying potential security secrets (API keys, passwords), ensuring data consistency across different environments (development, staging, production), and enforcing type safety for reliable deployments.

This guide identifies and avoids anti-patterns and common integration mistakes when working with Exa's neural search API. It covers crucial topics such as distinguishing between keyword and neural searches, properly retrieving content, handling date filters, and managing client instances. Use this when reviewing Exa-powered code, onboarding new team members, or auditing existing integrations for robustness and correctness.

This guide details setting up a robust CI integration pipeline for Speak language learning platform integrations. It utilizes GitHub Actions with mocked API testing and audio validation to ensure code reliability and functional correctness before deployment. Ideal for developers managing complex language learning products and APIs.

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.

Guides developers through receiving and responding to code review feedback: read carefully, restate requirements, verify against the codebase, evaluate correctness, push back with technical reasoning if needed, and implement one verified fix at a time.

A comprehensive engineering stack for designing and implementing robust, production-grade payment gateways for Software-as-a-Service (SaaS) platforms targeting the Pakistani market. It covers integrating major local providers like JazzCash and Easypaisa, handling complex payment flows (PKR billing, webhooks, idempotency), and performing essential financial reconciliation and auditing processes. This guide ensures high correctness and reliability in B2B/SaaS billing operations in Pakistan.

This tool allows users to systematically evaluate and rank the outputs of multiple agents within an AgentHub session. It supports metric-based evaluation (running custom commands for measurable metrics like speed or resource usage), or an advanced LLM Judge Mode. The LLM Judge compares results based on criteria like correctness, simplicity, and overall quality, helping users determine the best-performing agent for the task.

Guides structured reviews for Flutter/Dart pull or merge requests, checking correctness, security, style, tests, and documentation while verifying branch scope and CI health.

Test-Driven Development (TDD) is a software development methodology where tests are written before the actual code. It follows the Red-Green-Refactor cycle to ensure code quality and reliability. Use this skill when implementing new logic, fixing bugs, or modifying existing functionality to prove correctness. It helps prevent regressions and creates a robust codebase suitable for AI agents.

Conducts multi-dimensional code review with quality gates before merging any change. Evaluates correctness, readability, architecture, security, and performance, and proposes concrete structural remedies for flagged problems.