Skill UI

Provides production-ready, robust patterns for interacting with the SalesLoft REST API v2. This pattern solves common integration challenges by offering a type-safe client singleton, automatic iteration over paginated results, and intelligent handling of rate-limiting errors (429). Ideal for building scalable, reliable SalesLoft integrations using TypeScript or Python.

This guide provides a comprehensive setup for Continuous Integration (CI) and Continuous Deployment (CD) for SerpApi integrations. It outlines using fixture-based unit tests via GitHub Actions for Pull Requests (preventing API credit consumption) and controlled live integration tests on the main branch. This ensures robust and reliable search result parsing validation.

This guide provides a comprehensive reference for common errors encountered when using the SerpApi. It covers issues like invalid API keys, exhausted search credits, missing parameters, and CAPTCHA blocks. Use this when your automated search results are incorrect, empty, or return unexpected status codes, allowing you to quickly diagnose the root cause and implement fixes.

This guide details how to install the official SerpApi client library for Python and Node.js, and properly configure API key authentication. It is essential for any project requiring structured JSON results from major search engines like Google, Bing, and YouTube, ensuring secure and reliable search result scraping.

A comprehensive guide to optimizing search result retrieval using SerpApi. Learn advanced techniques such as multi-layer caching (LRU/Redis), utilizing the faster Google Light API, implementing parallel search execution, and minimizing data payload to drastically reduce latency and API credit consumption. Ideal for high-volume, low-latency search applications.

This guide outlines production-ready patterns for integrating SerpApi into robust search services. It covers essential best practices, including defining typed result interfaces for type safety, implementing LRU caching to conserve API credits, and building multi-engine abstractions to handle various search types (web, news, images). It also demonstrates asynchronous search processing for non-blocking operations.

This guide provides detailed instructions for migrating code from the legacy `google-search-results` package to the modern `serpapi` client library. It covers major API changes, including structural updates in Python and Node.js, transitioning from callback-based or class-based calls to modern, promise/async-await structures. Use this when upgrading dependencies or updating search logic.

A comprehensive guide to optimizing query performance in Snowflake. Learn advanced techniques such as implementing clustering keys, utilizing materialized views, leveraging result caching, and analyzing query profiles. This guide also covers best practices for warehouse tuning and optimizing common SQL anti-patterns to ensure efficient data querying and cost management.

This guide provides production-ready, idiomatic patterns for interacting with Snowflake using official SDKs (Node.js and Python). It covers essential patterns like implementing robust connection pooling, handling asynchronous query wrappers, efficiently streaming large result sets, and utilizing context managers for reliable resource cleanup. Use this when establishing team coding standards or optimizing data access performance.

This internal agent skill defines the structured procedure for displaying bug triage results in terminal markdown format. It generates comprehensive summaries of bug clusters, provides detailed views of evidence and reports, and handles the parsing and execution flow for user review commands (e.g., `details`, `file`, `dismiss`, `escalate`). It is a core component for automated bug triage and defect management workflows.

This guide details the UseCase pattern, an architectural pattern used to encapsulate specific business operations within the Webiny framework. Learn how to define UseCases as injectable dependencies, manage input and output using the structured Result<T, E> type, and implement robust, domain-specific error handling. Mastering this pattern is crucial for building highly modular, testable, and maintainable backend APIs.

This tool assists engineers in transforming vague work logs into structured, evidence-backed impact statements for professional documentation. It uniquely mines Copilot CLI session logs, git commits, and GitHub PRs to reconstruct forgotten work. It enforces a mandatory 'Action → Result → Evidence' impact contract, making it indispensable for preparing performance reviews, promotion packets, and self-assessments.