Skill UI

This skill implements Role-Based Access Control (RBAC) for Glean Enterprise Search. It establishes a secure framework by mapping permissions from various source systems (like AD/Okta) to the centralized search index. The functionality allows management of distinct roles (Super Admin, Admin, Content Manager), performing detailed document access checks via API, and maintaining comprehensive audit logs. This is crucial for maintaining compliance with standards like GDPR and SOC 2.

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 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.

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 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.

This skill provides comprehensive guidance and solutions for diagnosing and fixing common technical errors and exceptions encountered when integrating with the Grammarly API. It covers specific HTTP error codes (400, 401, 413, 429), addressing issues such as invalid tokens, payload size limits, rate limit exhaustion, and text formatting requirements. Use it when debugging failed API requests or troubleshooting complex integration workflows.

Utilizes Grammarly's advanced APIs to conduct comprehensive content auditing. This skill detects the likelihood of AI-generated content and checks the provided text against vast databases for plagiarism. It provides a combined score assessing overall writing quality, clarity, and academic integrity, making it essential for professional, academic, and published content review.

A guide and toolkit for optimizing Grammarly API costs in large-scale applications. It addresses common cost overruns associated with high text volume by implementing strategies such as result caching, chunking long documents, sample-based scoring, and setting usage budget alerts. Use this when integrating Grammarly into enterprise workflows or managing billing for high-volume content analysis.

Implements a robust data pipeline for handling large documents with the Grammarly API. This utility manages text chunking to comply with API character limits (max 100k), processes text in manageable segments, and aggregates multiple scoring metrics (like correctness, clarity, and tone) into a single, comprehensive report. Ideal for large-scale content analysis and data ingestion.