monitoring▌

Complete observability stack with structured logging, error tracking, and web analytics.

Monitoring deployed machine learning models ensures they continue to perform well in production, detecting data drift, concept drift, and performance degradation.

Set up synthetic monitoring to automatically simulate real user journeys, API workflows, and critical business transactions to detect issues and validate performance.

Comprehensive patterns for infrastructure monitoring, LLM observability, and quality drift detection. Each category has individual rule files in rules/ loaded on-demand.

You are an SLO (Service Level Objective) expert specializing in implementing reliability standards and error budget-based engineering practices. Design comprehensive SLO frameworks, establish meaningful SLIs, and create monitoring systems that balance reliability with feature velocity.

Guides ongoing brand monitoring—detecting impersonation, trademark infringement, counterfeit products, and brand abuse before they cause harm. Complements brand-protection (reactive: report, takedown); this skill covers proactive monitoring setup and tool selection.

Implement comprehensive database monitoring for performance analysis, health checks, and proactive alerting. Covers metrics collection, analysis, and troubleshooting strategies.

This skill provides comprehensive guidance for monitoring and observability workflows including metrics design, log aggregation, distributed tracing, alerting strategies, SLO/SLA management, and tool selection.

Guides building a holistic SEO data analysis system. Covers four core metrics (indexing, traffic, keywords, backlinks), benchmark setup, article database, tool selection, traffic diversification, penalty recovery, and work document management.

Comprehensive skill for security monitoring, threat detection, and incident response automation.