Skill UI

A comprehensive guide and code implementation for setting up observability, metrics, and alerting for Exa search integrations. This skill tracks crucial KPIs such as search latency (P50/P95), error rates, result usage, and cache hit ratios. It is essential for monitoring search quality, debugging performance bottlenecks, and managing API resource usage in production environments.

Provides comprehensive observability patterns for Firecrawl web scraping operations. It covers key metrics such as scrape success rates, credit consumption tracking, content quality assessment, and latency monitoring. Use this skill for implementing robust monitoring dashboards, setting up proactive failure alerts, and optimizing the reliability of web crawling workflows.

A comprehensive toolkit for monitoring the health and performance of Fireflies.ai integration. It tracks crucial metrics such as API connectivity, webhook delivery status, transcript processing latency, and seat utilization. Built using Prometheus metrics and suitable for Grafana dashboards, ensuring reliable data pipeline monitoring for engineering teams.

A comprehensive solution for implementing robust observability for Gamma API integrations. This pattern provides structured logging, detailed metrics tracking (including request counts, error rates, latency, and credit consumption), and health check endpoints. It is essential for monitoring the reliability and performance of your application when using the Gamma API in a production environment.

Automates Google Ads analytics via Rube MCP to list linked accounts, validate GA4 dimensions and metrics, and run tailored performance reports for campaigns while always fetching the latest tool schemas first.

This skill guides you through designing and implementing production-grade Grafana dashboards for comprehensive system observability. Learn to visualize critical metrics, track key performance indicators (KPIs), monitor infrastructure health (using principles like RED/USE), and build actionable alerts using Prometheus data sources. Ideal for DevOps engineers and Site Reliability Engineers (SREs).

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.

A comprehensive toolkit designed to monitor the health and performance of Guidewire InsuranceSuite. It tracks critical operational metrics including claim processing times, policy synchronization rates, billing accuracy, API latency, and batch job statuses. The system provides real-time alerting, structured logging, and health checks to maintain high service level agreements (SLAs) for complex insurance workflows.

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 guide for Site Reliability Engineers (SRE) covering the entire incident lifecycle. It provides structured methodologies for initial severity assessment, establishing an incident command structure, advanced observability-driven investigation (tracing, metrics, logs), systematic troubleshooting, and crucial post-incident root cause analysis (blameless post-mortems). Ideal for managing complex, large-scale system outages.

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.

A comprehensive utility for structured logging, request tracing, and diagnostic purposes when integrating with the Kling AI API. It automatically captures request/response pairs, task lifecycle events, and detailed timing metrics for every API call, making troubleshooting video generation pipelines efficient and systematic. Ideal for developers building robust, observable AI applications.