Skill UI

This utility provides a robust local development loop for integrating search APIs (like SerpApi). By caching real search responses into fixtures, developers can test search integrations, write unit tests, and develop features offline without incurring continuous API costs. It includes detailed instructions and code examples for Python and Node.js, ensuring cost-effective and reproducible testing.

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.

A comprehensive production readiness checklist for integrating SerpApi into live applications. This guide covers critical steps including secure API key management, setting up usage monitoring and credit budgets, implementing robust error handling, and optimizing performance for high availability. Use this when deploying or upgrading search features powered by SerpApi.

A comprehensive guide to managing API credit consumption and rate limits for SerpApi. This skill demonstrates how to implement robust throttling mechanisms using Python (Semaphore) and TypeScript (PQueue). It covers monitoring remaining account credits, slowing down requests to comply with per-second limits, and utilizing the search archive feature to optimize search volume and prevent unnecessary credit expenditure.

A comprehensive reference architecture for building scalable, production-grade search features using SerpApi. This blueprint covers essential components such as caching layers (Redis), multi-engine abstraction, SERP tracking pipelines, rate limiting, and credit budget management. Ideal for developers designing advanced search widgets, SEO tools, or complex search-powered 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.

This skill implements comprehensive SERP (Search Engine Results Page) monitoring using SerpApi. It covers asynchronous search result retrieval via polling and builds robust pipelines for tracking keyword rankings over time. Ideal for SEO tools, competitive intelligence, and automated performance monitoring.

This wrapper integrates Shopify's official AI Toolkit MCP server with Claude Code. It provides essential developer capabilities, including real-time GraphQL schema validation, Liquid template linting (checking for deprecated filters and anti-patterns), and instant search across Shopify's extensive documentation. Use this skill when setting up complex Shopify MCP integrations, ensuring code quality, or debugging API calls.

A comprehensive core workflow for managing the full e-commerce lifecycle within Shopify. It utilizes the GraphQL Admin API to query order data, process fulfillments (including tracking), create draft orders, and search/manage customer records efficiently. Ideal for building robust order management systems and complex e-commerce integrations.

This comprehensive skill guides users through the entire lifecycle of developing AI agent skills. From initial concept (Understand) to structured design, writing the core skill logic, rigorous testing, and final performance optimization. It ensures skills are robust, accurate, and highly triggerable across any domain, including engineering, content creation, and research.

This skill performs a structured Systematic Literature Review (SLR) by analyzing multiple academic papers on a given topic. It searches academic databases (like arXiv), extracts structured metadata (research questions, methodologies, key findings, limitations) from the entire set of papers, and synthesizes overarching themes and research trends. It is ideal for creating comprehensive literature surveys, annotated bibliographies, and cross-paper comparisons, outputting reports in professional citation formats like APA, IEEE, or BibTeX.