Skill UI

Automates Prerender tasks via Composio’s Rube MCP pipeline, covering tool discovery, connection checks, schema-compliant executions, and session-aware workflows that always search for current schemas before running multi-tool operations.

This skill provides comprehensive, production-grade patterns for asynchronous programming in Rust using the Tokio runtime. It covers essential concepts like tasks, channels, and streams, along with robust error handling techniques. Use it when developing concurrent network services, building complex async applications, or optimizing performance in Rust.

A comprehensive guide to integrating Sentry into continuous integration and continuous deployment (CI/CD) pipelines (e.g., GitHub Actions, GitLab CI, CircleCI). This skill automates complex tasks such as creating releases, uploading source maps for readable stack traces, associating commits, and tracking deployment environments, ensuring rapid and accurate debugging of production errors.

A guide to the Swift 6.2 concurrency model, which mandates default single-threaded execution to eliminate accidental data races. Learn how to use MainActor for isolated consistency and utilize the @concurrent attribute for explicitly offloading CPU-intensive tasks to background threads, ensuring safe and predictable application architecture.

This skill provides a systematic, multi-step approach to identify and resolve all failing unit tests within a codebase. It guides the user through smart error grouping (e.g., grouping by ImportError or AttributeError), prioritizing fixes from infrastructure to business logic, and ensuring thorough verification to prevent regressions. Ideal for use after major refactoring, debugging CI/CD failures, or when explicitly tasked with making the test suite pass.

A comprehensive framework guiding feature planning and implementation through adaptive phases: Specify, Design, Tasks, and Execute. It enforces highly structured development practices by using EARS notation, atomic tasks, deterministic Conventional Commits, and mandatory code validation scripts. Features include requirement traceability, automatic verification, and a self-improving lessons layer, ensuring quality and compliance throughout the lifecycle.

This skill provides comprehensive guidance for the entire lifecycle of Conductor tracks—the logical work units used to organize features, bugs, and refactors. It assists users in creating, reviewing, updating specifications (spec.md), developing plans (plan.md), and managing the status and metadata of tasks from initial conception through final completion, ensuring structured and traceable development processes.

This expert guide provides comprehensive best practices for developing robust, high-performance C++ code within Unreal Engine 5.x. It covers critical topics such as UObject lifecycle management, adhering to Epic Games' strict coding conventions, optimizing performance-critical sections (e.g., avoiding unnecessary Ticking), and correctly using soft references for asset loading. Use this skill for professional game development tasks requiring deep knowledge of the Unreal reflection system and advanced C++ patterns.

Implement robust, event-driven workflows for managing GPU resources on Vast.ai. Since native webhooks are unavailable, this skill uses Python polling to continuously monitor instance status changes (e.g., loading, running, exited). It enables complex automation tasks such as auto-recovering from spot preemption, handling lifecycle events, and tracking billing costs.

A comprehensive guide to building and deploying Vercel Edge Functions. These functions run on Vercel's global Edge Network (V8 isolates), providing ultra-low latency and zero cold starts. Learn how to use Web Standard APIs for critical tasks such as authentication, geolocation routing, A/B testing, and high-performance API middleware, drastically improving application speed and user experience.

This skill enables the creation and management of Windsurf Flows, providing automated workflows for handling repetitive development tasks. Users can utilize it to scaffold components, generate boilerplate code, run comprehensive test sequences, and execute complex multi-step operations with a single command, significantly streamlining the development process.

This guide outlines critical reliability patterns for integrating advanced AI coding assistants into professional development workflows. It emphasizes best practices such as mandatory Git checkpointing, utilizing dedicated feature branches, breaking large tasks into incremental steps, and implementing comprehensive validation gates (linting, testing). Use this when developing fault-tolerant, AI-assisted applications to prevent accidental code breaks and maintain project stability.