Skill UI

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 development workflow solution for integrating with the Glean enterprise search API. It enables developers to simulate local search responses, mock data sources, and test custom connectors and UIs without needing a live Glean deployment. Users can toggle between mock mode for rapid iteration and sandbox mode for validation against a real Glean instance, ensuring a fast feedback loop for building robust enterprise search applications.

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 comprehensive guide details the setup and deployment workflow for Glean enterprise search across development, staging, and production environments. It emphasizes strict environment isolation by managing separate datasources, API tokens, and configurations for each stage. The skill covers best practices for promotion workflows, validation steps, and error handling, ensuring search relevance and data integrity during scaling and updates.

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 document outlines the reference architecture for integrating diverse enterprise knowledge sources (such as Confluence, Jira, Notion, and Slack) into the Glean search index. It details the end-to-end data flow, covering connector frameworks, message queuing, permission synchronization (ACLs), and client-side search APIs. Designed to ensure continuous, reliable, and low-latency unified search across all corporate data.

A comprehensive guide detailing best practices, API version detection, and schema migration strategies for upgrading the Glean enterprise search platform. It covers critical steps like updating connectors, handling schema evolution (e.g., flat permissions to structured ACL), and implementing robust rollback strategies to ensure uninterrupted search functionality during major platform updates.

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.

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.

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.