Skill UI

A powerful skill that enables natural-language interaction with complex DeFi protocols across multiple EVM chains. It acts as a non-custodial agent, interpreting user prompts (e.g., 'swap X for Y') into simulated, multi-step transactions (like approve + swap). It utilizes account abstraction (EIP-7702/ERC-4337) to simulate and queue secure, user-signed transactions via the CLI, making advanced DeFi accessible from the terminal.

This module provides comprehensive abstractions, data types, and utilities for managing webhooks. It includes interfaces for defining payloads, tracking delivery status, dispatching events, and verifying incoming webhooks, enabling robust event-driven integration between services.

This skill enforces a high standard for all generated code, prioritizing correctness, clarity, and idiomatic patterns across all languages. It helps minimize code bloat by removing unnecessary abstractions and verbose patterns, while also optimizing the agent's output by using targeted diffs and eliminating excessive conversational overhead. Use this for all code generation, refactoring, or review tasks.

This skill provides comprehensive code review capabilities for analyzing git diffs or specific file scopes. It goes beyond simple syntax checks, assessing code for reuse opportunities, quality issues (e.g., redundant state, leaky abstractions), efficiency bottlenecks, and adherence to standards. It utilizes multiple specialized sub-agents (Reuse, Quality, Efficiency, Clarity) to perform parallel, read-only audits. Optionally, it can apply high-confidence, behavior-preserving fixes upon user request.

This skill enforces highly efficient coding practices, guiding the AI to write the simplest, most direct code possible. It strictly follows an 'Efficiency Ladder' (YAGNI, reusing existing code, standard library, etc.) to prevent over-engineering, unnecessary abstractions, and boilerplate. Use this when the goal is maximum efficiency with minimal code.

This skill provides deep insights into idiomatic Rust development. It covers core concepts like ownership and borrowing to prevent data races, robust error handling using `anyhow` and `thiserror`, advanced pattern matching with enums, and utilizing traits and generics for safe abstraction. Master these patterns when writing or refactoring complex, high-performance, and reliable Rust applications.

This comprehensive guide helps authors position and structure systems-focused manuscripts for top-tier conferences like SOSP. It covers defining the core contribution (abstraction, architecture, mechanism), optimizing the narrative for systems reviewers, and strengthening the empirical evidence bar in operating systems and distributed computing.

Generates highly accurate, structured summaries from complex clinical notes, such as discharge summaries, SBAR reports, or problem lists. The core function is to anchor every generated claim to a specific source span within the original text, guaranteeing verifiability and eliminating hallucination. Essential for clinical documentation drafting and chart abstraction.

This skill generates the complete native source code for a third-party Platform Abstraction Module (PAM) control, including iOS Obj-C, Android Kotlin, required podspecs, and a manifest contract. It bypasses TypeScript layers, focusing purely on the native build surface and the dispatch contract (`manifest.json`). This is used by developers to scaffold the foundational, OS-specific code necessary for integrating custom, native modules into a React Native environment.

This documentation serves as a catalog for integrating various AI powerups and structured features into administrative web panels. It guides developers on locating and correctly importing specific abstractions, such as `GetSettingsFeature`, ensuring type safety and proper implementation when extending system capabilities.

This catalog provides fundamental abstractions for integrating and managing a structured GraphQL playground within an administrative panel. It includes essential components like client factories and tab registries, allowing developers to easily access and interact with complex GraphQL APIs in a standardized, scalable manner.

This abstraction provides a centralized mechanism for handling every incoming WebSocket message. Instead of directly subscribing to the service, handlers register themselves and are responsible for filtering the event payload based on a specific 'action'. This ensures components react only to the events they are interested in, promoting efficient and decoupled application architecture.