Skill UI

This skill provides comprehensive observability tools for Groq API integrations. It instruments the client to track critical performance metrics such as Time to First Token (TTFT), total latency, token throughput, and estimated cost. Furthermore, it sets up Prometheus exporters and alert rules to monitor rate limit status and detect performance degradation in real-time, ensuring robust production monitoring.

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 guides the implementation of comprehensive, real-time monitoring for APIs. It covers collecting key metrics (latency, error rates, request rate), setting up health and readiness endpoints, building detailed Grafana dashboards, and implementing SLO-based alerting (following the RED method). Use this when you need to ensure API reliability, high availability, and performance in a production environment.

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.

Monitor the operational health of PostHog integrations by tracking critical metrics. This skill checks event ingestion rates, feature flag evaluation latency, and monitors billing volume consumption. It also provides rules for Prometheus/Grafana alerting, ensuring data pipelines remain stable and alerting on potential performance bottlenecks or exceeding API limits.

This guide provides comprehensive methods for load testing and capacity planning of Vercel serverless deployments. Learn how to use professional tools like k6 and autocannon to simulate realistic traffic spikes, measure latency (P95, P99), analyze cold start impact, and tune concurrency settings (Fluid Compute) to ensure optimal application performance and scalability under heavy load.

A comprehensive guide for optimizing query latency in Qdrant. This resource explains advanced performance tuning techniques, focusing on critical resource management like RAM utilization, segment count adjustments, and HNSW parameter tuning. It details strategies for scaling, quantization, and hardware considerations (e.g., local NVMe) to ensure consistently low-latency search performance, and outlines common pitfalls to avoid.

Provides a comprehensive reference architecture for deploying highly scalable, low-latency applications on Fly.io. It details best practices for multi-region web tiers, achieving high availability with Postgres read replicas, implementing Redis caching, managing background workers, and securing connectivity via private internal networking for robust production systems.

This guide provides comprehensive guidance on scaling Qdrant, a vector database. It helps users diagnose performance bottlenecks and determine the optimal horizontal or vertical scaling strategy. Strategies are categorized based on specific needs: increasing data volume, improving query throughput (QPS), minimizing query latency, or managing high query result volumes.

This guide helps define a complete observability stack for any service. It systematically covers the Four Golden Signals (latency, traffic, errors, saturation), guiding users to define core metrics, set Service Level Objectives (SLOs), configure actionable alerting rules, and structure comprehensive monitoring dashboards. Essential for establishing robust SRE practices and documenting system health standards.

This skill guides the creation of a comprehensive Service Level Objective (SLO) document. It helps define Service Level Indicators (SLIs) by identifying critical user journeys and measuring key metrics like success rate and latency. It calculates the allowed "error budget" and establishes a burn rate policy to maintain service reliability while enabling feature velocity and principled trade-offs.

A rigorous, data-driven methodology for improving system performance. Use this skill when aiming to significantly increase speed, reduce latency, or lower operational cost. It establishes a measurable baseline, generates testable variants based on hypotheses, runs controlled benchmarks, and validates the best performing solution through iterative testing and code promotion gates.