Skill UI

Enables Claude to monitor and analyze application CPU usage, detect hotspots, inefficient loops and regexes, and offer optimization strategies such as asynchronous processing or algorithmic improvements to boost performance.

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.

Guides performance testing teams through network latency tester tasks by suggesting best practices, generating production-ready scripts/configs, and validating results across load, stress, benchmarking, and monitoring scenarios.

Expert skill in designing and implementing comprehensive observability stacks for enterprise microservices. Covers advanced monitoring (Prometheus, Grafana), distributed tracing (Jaeger, OpenTelemetry), centralized logging (ELK, Splunk), and defining SLIs/SLOs. Ideal for ensuring high availability, mastering incident response, and performing reliability engineering on large-scale, distributed applications.

This skill specializes in implementing Service Level Objectives (SLOs) and Service Level Indicators (SLIs) to enforce reliability standards. It guides users in designing comprehensive SLO frameworks, calculating error budgets, and building robust monitoring systems. Use this when you need to establish data-driven reliability targets, balance feature velocity with technical health, and align service performance directly with core business objectives.

This skill provides comprehensive, structured audit logging for OpenRouter API interactions. It automatically captures essential metadata like total cost, token usage, model details, latency, and status, while also performing PII redaction. The resulting audit trail is stored in an append-only SQLite database, making it ideal for building compliance-ready systems, detailed cost monitoring, and deep debugging of production environments.

This skill provides comprehensive tools to monitor the real-time availability, status, and performance of various large language models (LLMs) accessed via OpenRouter. It supports active health probes, catalog checks, and scheduled monitoring via Python and Bash, ensuring robust fault detection and failover logic for production systems that rely on external AI APIs.

Guides the user through configuring and tuning database connection pools across languages, from selecting pool sizes to setting timeouts and monitoring usage to boost application performance.

Automated skill that assists with percentile analyzer workflows in performance testing, guiding users through load, stress, benchmarking, and monitoring setups with best practices.

Guides engineers through bundle analysis, lazy loading, image optimization, caching headers, virtualized lists, and Core Web Vitals monitoring so web apps stay fast and scalable for production traffic.

Provides a comprehensive monitoring solution for the Perplexity Sonar API. It tracks critical performance metrics, including latency (p95), cost per query, error rates, and citation quality. This skill is essential for implementing production monitoring dashboards, configuring proactive alerts (e.g., high latency, zero citations), and ensuring the API's stability and high-quality output in real-world applications.

This comprehensive checklist guides the deployment of Perplexity Sonar API integrations to a robust production environment. It covers critical aspects including API key management, robust code quality practices (retries, rate limiting), performance optimization (caching, streaming), detailed monitoring setup, and cost control strategies. Use this checklist when preparing for a major Perplexity feature launch or going live to production.