Skill UI

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 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.

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 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.

This bundle collects comprehensive diagnostic evidence for troubleshooting Grammarly API integration issues. It captures API connectivity status, text check responses, authentication details, and rate limit headers. Use this when encountering persistent errors, preparing detailed support tickets, or performing automated health checks on Grammarly services.

A comprehensive guide for configuring and managing Role-Based Access Control (RBAC) in Grammarly Enterprise deployments. This covers role hierarchies (Org Admin, Team Admin, Member, Guest), implementing secure API access, auditing user permissions, and maintaining compliance standards like HIPAA and SOC 2.

This skill provides a comprehensive set of APIs for advanced writing analysis using Grammarly. It allows users to score text based on engagement, correctness, clarity, and tone. Furthermore, it includes functionality for AI detection and sophisticated plagiarism checking. It is ideal for developers needing to build content verification, grammar-checking, or academic integrity tools.

This comprehensive runbook provides structured procedures for diagnosing and responding to critical failures when integrating with the Grammarly API. It covers various incident types, including full API outages, OAuth token expiration, rate limit storms, and suggestion quality degradation. The guide includes severity classification, diagnostic `curl` commands, detailed playbooks, and post-incident documentation steps to ensure service resilience and minimal downtime.