Skill UI

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.

Guides teams through selecting a RUM platform, defining Core Web Vitals and custom events, and wiring JavaScript instrumentation so actual user performance data can be collected and analyzed for web apps.

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, production-ready toolkit for robustly debugging and monitoring Linear API integrations. This bundle includes features like an instrumented client wrapper for detailed request/response logging, a performance request tracer with summary statistics (avg, p95, max latency), a health check utility, and an environment validator. Ideal for developers setting up logging, performance monitoring, or debugging utilities in a live SaaS environment.

This skill provides a comprehensive framework for monitoring Mistral AI API usage in production. It includes an instrumented client wrapper (TypeScript) to automatically capture critical metrics like latency, token consumption, cost, and error rates. Furthermore, it integrates with Prometheus for metric storage and Grafana for visualization, allowing users to set up sophisticated alerting rules for performance degradation, rate limiting, or cost spikes.

This skill provides comprehensive monitoring and comparative analysis of cross-chain bridges in the DeFi ecosystem. It tracks key metrics such as Total Value Locked (TVL), transaction volume, typical fees, and real-time transfer status across major networks (e.g., Ethereum, Arbitrum, Polygon). Use this tool to research bridge safety, compare transfer routes before depositing funds, or track specific transaction hashes across multiple protocols like Wormhole, LayerZero, and Stargate.

Documents setting up OpenTelemetry tracing/metrics, Prometheus/Grafana monitoring, structured logging, and alerting rules so teams can instrument services, track performance, and respond to incidents.

A comprehensive utility designed to monitor, analyze, and predict blockchain gas costs across multiple major chains (e.g., Ethereum, Polygon, Arbitrum). It fetches real-time pricing, estimates transaction costs in both ETH and USD, and identifies the optimal time windows for executing DeFi or NFT transactions, ensuring maximum cost efficiency.

Implements schema.org JSON-LD across webpages so search engines grasp content intent and serve rich results like FAQs, breadcrumbs, and products, with validation checklists and contextual guidance.

An advanced skill for Scrum Masters that uses Python scripts to analyze raw sprint data exported from tools like Jira. It provides comprehensive insights including Monte Carlo velocity forecasting, a multi-dimensional team health score (0-100), and tracking of action items and recurring themes from retrospectives. Ideal for process improvement, sprint planning, and stakeholder reporting.

Master advanced error capture and context enrichment using Sentry SDKs for both TypeScript and Python. This skill teaches how to reliably capture exceptions, track non-exception events (messages), and enrich reports with structured data like user context, tags, breadcrumbs, and custom fingerprints. It is essential for dramatically improving the ability to diagnose complex production issues quickly.

This skill provides a comprehensive walkthrough for setting up and testing Sentry error monitoring using both Node.js and Python SDKs. It demonstrates how to capture various event types, including simple messages and full exceptions, and crucially, how to enrich these events with user context, searchable tags, and detailed breadcrumbs. This is essential for verifying robust error logging and monitoring in a production environment.