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Azure Monitor Query SDK for Python

azure-monitor-query-py

sickn33/antigravity-awesome-skills

A comprehensive Python SDK for interacting with Azure Monitor services. It enables developers to execute complex Kusto Query Language (KQL) queries against Log Analytics workspaces and retrieve various resource metrics (CPU usage, network traffic, etc.). Ideal for building robust monitoring dashboards, automated alerting systems, and custom data analysis pipelines in cloud environments.

Intelligent Alerting Rule Builder

creating-alerting-rules

jeremylongshore/claude-code-plugins-plus-skills

Automates intelligent alert rule creation for SREs and DevOps, guiding through thresholds, routing, escalation, and runbooks to monitor latency, error rates, throughput, resource use, availability, and SLO violations.

Customer.io Observability and Metrics Setup

customerio-observability

jeremylongshore/claude-code-plugins-plus-skills

This skill provides a comprehensive solution for implementing observability for Customer.io integrations. It covers setting up Prometheus metrics (latency, request counts, bounces), structured JSON logging with PII redaction, and OpenTelemetry tracing. Use it when building robust, production-grade monitoring dashboards and alerting systems for Customer.io APIs.

Deepgram Observability Stack Setup

deepgram-observability

jeremylongshore/claude-code-plugins-plus-skills

Implements a comprehensive observability stack for Deepgram integrations. It utilizes Prometheus metrics for tracking request rates, latency, and costs, OpenTelemetry for distributed tracing, Pino for structured logging, and AlertManager rules for proactive alerting. Essential for production monitoring, dashboard setup, and performance analysis of Deepgram API usage.

Error Handling Observability Guide

CloudAI-X/claude-workflow-v2

Provides structured error handling patterns, logging, retry/circuit breaker strategies, and observability workflows across JavaScript, Python, and Go; ideal when designing resilience, retries, aggregated logging, or alerting.

Evernote Observability Setup

evernote-observability

jeremylongshore/claude-code-plugins-plus-skills

Provides a comprehensive framework for implementing observability in Evernote integrations. It covers metrics collection (Prometheus), structured JSON logging, distributed tracing (OpenTelemetry), and defining health/readiness endpoints. Use this when setting up production monitoring, alerting, and performance tracking for mission-critical Evernote APIs.

Monitoring Exa Search Performance Metrics

exa-observability

jeremylongshore/claude-code-plugins-plus-skills

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.

Guidewire Observability Monitoring Toolkit

guidewire-observability

jeremylongshore/claude-code-plugins-plus-skills

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.

Ideogram API Observability and Monitoring

ideogram-observability

jeremylongshore/claude-code-plugins-plus-skills

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.

Observability for SaaS Platform Monitoring

juicebox-observability

jeremylongshore/claude-code-plugins-plus-skills

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.

AI Cost Control and Budget Management

klingai-cost-controls

jeremylongshore/claude-code-plugins-plus-skills

This utility provides robust cost control mechanisms for integrating AI generation APIs. It implements daily budget enforcement, per-request cost estimation, and usage alerting. Developers can use it to prevent unexpected overspending and effectively manage credits and resources when building AI-powered applications.

Setting Up Lokalise Observability Metrics

lokalise-observability

jeremylongshore/claude-code-plugins-plus-skills

This skill provides a comprehensive guide to setting up full observability for Lokalise integrations. It covers crucial metrics, traces, and alert configurations, monitoring everything from API response times and rate limit consumption to translation progress and webhook reliability. Ideal for developers building robust monitoring dashboards and alerting systems for localization workflows.

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