Skill UI

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 framework provides a comprehensive methodology for defining, measuring, and implementing Service Level Indicators (SLIs), Service Level Objectives (SLOs), and managing error budgets. It helps engineering teams quantify service reliability (e.g., availability, latency) and establish clear, measurable targets (SLOs). By tracking error budgets, teams can strategically balance rapid feature development (innovation velocity) against maintaining high system stability and reliability.

Provides SRE guidance for defining SLIs/SLOs, managing error budgets, automating toil, and configuring monitoring/alerting so teams can scale reliable services and respond to incidents effectively.

Defines and monitors SLAs, SLIs, and SLOs so teams can set performance targets, track metrics like error budgets and availability, and react before issues impact customers.

This skill provides comprehensive observability tooling for Go services, covering the five essential signals: structured logging (slog), Prometheus metrics, OpenTelemetry tracing, continuous profiling, and RUM. Use it for proactive production monitoring, correlating logs with traces, setting up SLOs, and ensuring all new features are fully instrumented for robust diagnosis.

This skill integrates LangChain 1.0 and LangGraph 1.0 into standard OpenTelemetry (OTEL) backends (e.g., Jaeger, Honeycomb, Tempo). It solves critical observability gaps by ensuring correct prompt content capture, propagating spans across complex subgraphs, and providing five LLM-specific Service Level Objectives (SLOs) like latency and cost. Ideal for multi-tenant, compliance-sensitive, or existing OTEL stack environments where LangSmith is unsuitable.

This rigorous review simulates an interrogation by a Chief AI Officer (CAIO). It pressure-tests any AI plan across six critical dimensions: evaluation metrics, failure mode handling (SLOs), international regulatory compliance (e.g., EU AI Act), technical architecture (API vs. fine-tuning), cost economics, and necessary team roles. Use this when ensuring AI features are robust, safe, and commercially sound before launch or major investment.

This tool simulates a Chief Technology Officer's comprehensive review, stress-testing any major technical plan. It systematically interrogates critical areas including scaling limits (scaling cliffs), accumulated technical debt, team capacity planning, build-vs-buy economics, system reliability (SLOs), and security compliance. Use it before committing to core architecture changes or making high-stakes engineering decisions.

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 generates a comprehensive, actionable performance budget document for any web service or application. It moves beyond wish lists by establishing measurable performance constraints, covering key user journeys, Core Web Vitals (LCP, CLS, INP), backend SLOs (P95, P99), and architectural limitations. It guides teams on setting targets, measurement tools, and incident response processes when budgets are breached.