Skill UI

A comprehensive guide for migrating search functionality from alternatives like Elasticsearch, Typesense, or Meilisearch to Algolia. It details the necessary steps, including data migration, query translation (e.g., ES to Algolia), and implementing a zero-downtime swap using the Strangler Fig pattern.

A comprehensive skill designed for finance-related information retrieval. It provides dual search capabilities: real-time web searching across multiple engines (Jina, DDG, Baidu) for general market knowledge, and Retrieval-Augmented Generation (RAG) for querying local, proprietary document stores (e.g., daily news). Ideal for financial analysts needing both broad market context and specific internal data insights.

This skill provides comprehensive access to Bright Data's SERP API and Web Scraper API. It allows users to collect structured search engine results (Google, Bing, Yahoo) synchronously, or trigger large-scale, asynchronous data collections with webhooks. Ideal for market research, SEO analysis, and large-scale data scraping projects.

This comprehensive runbook provides detailed procedures for diagnosing and resolving critical incidents within the Glean enterprise search platform. It covers all severity levels (P1-P4) and failure modes, including API outages, connector synchronization failures, indexing backlogs, and permission drift. It includes diagnostic steps, advanced troubleshooting playbooks, and communication templates for technical support and engineering teams.

A senior-level tool for performing deep-dive technical SEO audits and site architecture mapping. It analyzes crawl health, indexing blocks, sitemap integrity, and structural issues (like canonicalization, link flow, and crawl budget waste). Use this to diagnose complex Google Search Console errors and propose a full, engineered roadmap for website stabilization and optimization.

This comprehensive curriculum provides a detailed roadmap for mastering exploit development and vulnerability research. It covers fundamental techniques like fuzzy testing using AFL++, and progresses through advanced topics such as in-process fuzzing with Google FuzzTest. It is ideal for structuring professional security training programs or onboarding new research engineers.

Qdrant is a high-performance vector database. This client SDK suite provides official, easy-to-use libraries for multiple programming languages (including Python, Java, Go, and TypeScript). It enables developers to seamlessly integrate sophisticated vector search capabilities into their applications, supporting both REST and gRPC APIs. Ideal for building semantic search engines, recommendation systems, and advanced AI applications.

This workflow utilizes the SerpApi to perform advanced, multi-engine searches across platforms including YouTube, Bing, Google News, Google Shopping, and Google Maps. It is essential for data scraping, SEO research, and building comprehensive search features that require aggregating results from diverse sources beyond standard Google search. Use it to collect videos metadata, product pricing, local business details, and news articles efficiently.

Utilize this tool to programmatically access and parse search engine result pages (SERP) data from multiple sources, including Google, Bing, YouTube, and Google News. It returns comprehensive, structured JSON data, allowing developers and marketers to extract organic results, knowledge graph entries, ads, and local information for advanced SEO analysis, market research, and data science projects. Supports both Python and Node.js implementations.

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 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.