Skill UI

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 monitoring capabilities for GPU instances running on Vast.ai. It collects key metrics including GPU utilization, instance uptime, temperature, and accumulated costs. Use this when setting up operational monitoring dashboards, configuring critical alerts (e.g., idle GPUs, high temperature, budget overruns), or automating resource usage tracking in a cloud computing environment.

A comprehensive guide to optimizing cloud expenditure on the Vercel platform. It details how to understand the Fluid Compute pricing model, identify key cost drivers (such as function execution time, bandwidth, and middleware invocations), and implement best practices like edge caching, memory right-sizing, and aggressive asset caching. Use this when analyzing high Vercel billing, implementing cost controls, or managing a SaaS budget.

This skill enforces disciplined SLO definition based on SRE principles, ensuring that reliability promises are measurable and actionable. It guides users in selecting the right SLI (Service Level Indicator), accurately calculating error budgets, setting multi-window burn-rate alerts, and reviewing existing SLO documents for common architectural flaws (e.g., targets too aggressive, wrong SLI selection). Ideal for defining, reviewing, and maintaining service reliability commitments.

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.