Skill UI

This runbook provides a structured guide for responding to critical Guidewire production incidents. It covers the entire lifecycle of incident management, including initial triage (checking monitoring, API errors, and batch logs), diagnosing performance degradation (JVM metrics and query performance), implementing mitigation steps, and proper escalation procedures to Guidewire Support.

Provides production-ready patterns for integrating with the Guidewire insurance platform. This SDK abstracts the complexity of hybrid APIs (REST/SOAP), offering a structured client, robust error handling (retry logic, conflict detection), and a fluent query builder for consistent data access across policies and claims.

A comprehensive guide for implementing standardized, robust error handling in modern APIs. It utilizes RFC 7807 Problem Details, centralized middleware, and typed error classes to convert diverse failure types (validation, auth, database, upstream service) into consistent, machine-readable responses with correct HTTP status codes. Essential for building production-grade, resilient backends.

This comprehensive guide serves as a quick reference for diagnosing and resolving common Ideogram API errors and exceptions. It covers critical issues such as authentication failures (401), rate limiting (429), safety check failures (422), and incorrect parameter formats (400). Use this resource when debugging failed image generation requests or integrating Ideogram functionality into an application.

A comprehensive guide to implementing robust observability for Ideogram AI image generation APIs. Learn how to track critical metrics such as generation duration (latency), estimated API costs, error rates, and content safety rejections. This skill involves instrumenting API calls using TypeScript, integrating cost estimation, and setting up Prometheus alerting rules to ensure stable and reliable AI operation.

A comprehensive checklist and guide for deploying and maintaining Ideogram image generation services in a live production environment. It covers essential steps including API key management, request handling best practices, ensuring image persistence in durable storage, comprehensive error handling (401, 422, 429), and defining robust rollback procedures.

This skill provides a comprehensive diagnostic reference for common errors and exceptions encountered with the Instantly.ai API v2. It covers critical areas including HTTP status codes (400, 401, 429, 500), campaign lifecycle issues (e.g., stuck in draft), account health checks, lead duplication handling, and implementing rate limit backoff. Use this when debugging failed API requests or troubleshooting complex campaign, account, or lead management issues.

This skill provides robust patterns for handling API rate limits (e.g., 429 errors) when building high-throughput integrations. It demonstrates best practices including exponential backoff with jitter, request queuing, concurrency control, and specific endpoint throttling mechanisms, ensuring reliable service connectivity.

This comprehensive guide addresses common technical and business logic errors encountered when integrating with the Juicebox API. It covers HTTP status codes (e.g., 401, 403, 429), including authentication failures, quota exhaustion, rate limiting, data validation issues, and analysis timeouts. The resource provides detailed causes, specific fixes, and best practices for building reliable and robust integrations into recruiting and research workflows.

This guide provides comprehensive metrics, instrumentation code (TypeScript), health check logic, and alerting rules for monitoring a SaaS platform like Juicebox. It covers critical metrics such as search latency (p95), data ingestion rates, API error rates, and quota usage, ensuring the maintenance of an optimal user experience and data integrity. Use this to establish robust operational monitoring dashboards and alerts.

This comprehensive checklist guides the operational validation of the Juicebox AI talent search and analysis platform in a production environment. It ensures system reliability, data security, and service continuity by reviewing critical areas such as API key management, rate limit handling, robust error detection (circuit breakers, exponential backoff), data encryption (GDPR/CCPA compliance), and comprehensive monitoring setup. It is essential for preventing service failures and maintaining search data integrity.

This guide provides comprehensive strategies and TypeScript implementations for managing rate limits when interacting with the Juicebox AI API. It includes a token bucket rate limiter class, a robust retry mechanism for handling 429 errors and transient failures, and utilities for efficient batch processing of large datasets. Use this when building high-throughput applications that rely on Juicebox data analysis or enrichment endpoints.