Skill UI

This guide provides comprehensive strategies for managing Algolia's rate limits and throttling mechanisms. It covers per-API-key limits (e.g., max queries per hour) and server-side indexing queue limits. Learn how to implement robust backoff strategies, exponential backoff, and controlled batch indexing using TypeScript examples to ensure application stability under high load.

This comprehensive guide provides a production-ready reference architecture for implementing scalable search using Algolia. It details best practices for index design (including multi-index strategies), establishing robust data pipelines from source databases, structuring the service layer (search, indexing, settings), and integrating the frontend for optimal performance.

This guide provides production-ready, high-quality patterns for integrating with Algolia Search v5. It covers critical architectural aspects such as implementing singleton client management, performing strongly typed searches, robust error handling (including specific API error codes), efficient batch indexing, and handling multi-tenant environments. Use these patterns to refactor existing code and establish reliable coding standards.

Automates the CI/CD process for Glean enterprise search connectors. This tool runs comprehensive unit tests with mocked responses on every Pull Request, and executes integration tests against a staging Glean instance upon merging to the main branch. It validates connector data transforms, indexing API calls, and performs search relevance regression testing to ensure high search quality.

A comprehensive guide designed to diagnose and resolve common technical errors encountered when integrating with the Glean enterprise search API. It covers critical areas such as token type mismatches, indexing pipeline failures, permission restrictions, and rate limiting, providing actionable fixes for maintaining seamless search functionality.

This skill demonstrates the complete workflow for integrating with the Glean platform. It guides users through three core steps: setting up a custom data source using the Indexing API, programmatically indexing documents, and executing advanced search queries using the Client API. This is essential for developers starting new Glean connectors or needing to test their search pipelines.

This comprehensive guide details how to authenticate and configure connections to the Glean enterprise search platform. It covers the distinct setup for the Indexing API (used for pushing data into the search index) and the Client API (used for searching and retrieving results). Essential for developers integrating custom data sources or building advanced search features.

A comprehensive technical guide detailing the process of migrating large-scale enterprise search platforms from sources like Elasticsearch or Algolia to Glean. It covers data export, schema transformation, using bulk indexing APIs, creating data sources, and validating search quality for smooth, large-scale deployment.

This tool provides comprehensive observability for enterprise search systems (Glean). It tracks critical metrics such as search latency (p95), zero-result query rates, indexing throughput, and stale content ratios. By monitoring connector sync status and detecting indexing degradation, it allows teams to proactively maintain search relevance and ensure sub-second response times, preventing silent data quality decay.

This skill provides comprehensive guidelines and implementation examples for managing rate limits when interacting with the Glean Indexing and Search APIs. It includes a custom RateLimiter class, robust retry logic (handling 429 errors and server errors), and best practices for batch processing large datasets, ensuring stable, high-volume data ingestion pipelines.

This skill demonstrates how to build a robust, event-driven indexing system for Glean. It processes real-time webhooks received from various source systems (like GitHub, Notion, Confluence), handling events such as document indexing, permission changes, and search anomalies. The solution includes critical components like signature verification and idempotent message handling to ensure data consistency and reliability.

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.