Skill UI

This comprehensive guide demonstrates how to achieve highly testable Swift code using protocol-based dependency injection. By abstracting external concerns—such as file systems, networking, and external APIs—into small, focused protocols, developers can write deterministic unit tests that mock side effects and error paths, ensuring robust architecture across different environments (App, Test, Preview).

A robust internal utility for integrating system status updates with Feishu/Lark. It allows other skills to send structured notifications (e.g., experiment complete, checkpoint waiting, review scores) via webhooks. Supports both 'Push only' (fire-and-forget) and 'Interactive' (bidirectional polling) modes, guaranteeing zero side effects if configuration is missing.

This technique audits user interaction flows (like button clicks) to detect subtle bugs that static analysis often misses. It specifically tracks the full sequence of state changes, identifying instances where later actions unintentionally cancel, contradict, or leave the UI in an inconsistent state due to state store side effects or race conditions. Ideal for debugging complex state management architectures (Redux, Zustand).

A comprehensive guide detailing best practices for using Riverpod in Flutter and Dart. Learn how to define various providers (Future, Stream, Notifiers), manage state lifecycles, handle side effects, combine data streams, and utilize the `ref` object correctly for robust application state management.

This technique guides users to find obscure bands by utilizing unique, non-musical personal experiences of the band members (e.g., major surgery, relocation, unique side careers). Instead of searching directly for band names or discography details, the user should identify the most distinctive life event mentioned in the query. Searching for this unusual personal anchor point first is highly effective, as it leads to the member's identity, which can then be used to pinpoint the correct band.

This skill provides comprehensive guidance on optimizing operational costs on the Apify platform. It covers analyzing Compute Units (CU) usage, managing proxy consumption (residential vs. datacenter), and optimizing scraping logic (memory, concurrency) to ensure efficient and cost-effective web scraping workflows.

This guide provides best practices for optimizing HubSpot integration costs and performance. Learn to manage API call volume by implementing batch reads, using server-side search filters, and leveraging webhooks instead of continuous polling. It covers crucial concepts like rate limit monitoring and caching to prevent 'Too Many Requests' errors (429), ensuring stable and cost-effective development.

This guide provides comprehensive strategies for optimizing API usage and managing costs when integrating with Miro. It covers credit monitoring, advanced techniques like caching, server-side filtering, batching, and subscribing to webhooks. By implementing these best practices, developers can drastically reduce API call volume, stay within rate limits, and ensure highly efficient and cost-effective application development.

This comprehensive skill provides foundational knowledge in core marketing concepts, psychological principles, and strategic frameworks. It covers the entire customer journey, from initial awareness (TOFU) through consideration, decision, and advocacy. Users can apply advanced models like the Funnel Model, Value Proposition Canvas, and Positioning Statements to structure effective campaigns, refine messaging, and measure key business results.

This skill provides a scaffold for building complex, composable recommendation, ranking, and feed pipelines. It implements the robust six-stage pattern (Source→Hydrator→Filter→Scorer→Selector→SideEffect), which is the industry standard for determining "top K items." Use it for social feeds, content CMS ranking, RAG reranking, or any system needing complex candidate prioritization.

This skill generates a detailed, step-by-step, interprocedural execution trace for a given piece of code and specific input scenario. It resolves name ambiguities, tracks type transitions, and highlights non-obvious behaviors (like implicit coercions or side effects). Use it when a user is confused about how code behaves, allowing for a deep dive into the actual execution path to clarify assumptions versus reality.

This technique audits user interaction flows (like button clicks) to detect subtle bugs that static analysis often misses. It specifically tracks the full sequence of state changes, identifying instances where later actions unintentionally cancel, contradict, or leave the UI in an inconsistent state due to state store side effects or race conditions. Ideal for debugging complex state management architectures (Redux, Zustand).