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Found 65 Skills
Set up monitoring, logging, and observability for applications and infrastructure. Use when implementing health checks, metrics collection, log aggregation, or alerting systems. Handles Prometheus, Grafana, ELK Stack, Datadog, and monitoring best practices.
Set up Prometheus for comprehensive metric collection, storage, and monitoring of infrastructure and applications. Use when implementing metrics collection, setting up monitoring infrastructure, or configuring alerting systems.
Monitoring guidelines for applications and infrastructure including metrics collection, alerting strategies, and SLO-based monitoring
Monitor management - create, update, mute, and alerting best practices.
Expert-level Prometheus monitoring, metrics collection, PromQL queries, alerting, and production operations
Observability visualization with Grafana and LGTM stack. Dashboard design, panel configuration, alerting, variables/templating, and data sources. USE WHEN: Creating Grafana dashboards, configuring panels and visualizations, writing LogQL/TraceQL queries, setting up Grafana data sources, configuring dashboard variables and templates, building Grafana alerts. DO NOT USE: For writing PromQL queries (use /prometheus), for alerting rule strategy (use /prometheus), for general observability architecture (use senior-software-engineer with infrastructure focus). TRIGGERS: grafana, dashboard, panel, visualization, logql, traceql, loki, tempo, mimir, data source, annotation, variable, template, row, stat, graph, table, heatmap, gauge, bar chart, pie chart, time series, logs panel, traces panel, LGTM stack.
Expert-level Grafana dashboards, visualization, data sources, alerting, and production operations
Production observability with structured logging, metrics collection, distributed tracing, and alerting
Set up monitoring, logging, and alerting for infrastructure and applications. Use when implementing observability, creating dashboards, or configuring alerts.
Prometheus metrics and PromQL queries. Use when writing PromQL queries, creating recording or alerting rules, debugging metric scraping issues, or understanding counter/gauge/histogram behavior.
Know when your AI breaks in production. Use when you need to monitor AI quality, track accuracy over time, detect model degradation, set up alerts for AI failures, log predictions, measure production quality, catch when a model provider changes behavior, build an AI monitoring dashboard, or prove your AI is still working for compliance. Covers DSPy evaluation for ongoing monitoring, prediction logging, drift detection, and alerting.
Automatically discover observability and monitoring skills when working with Prometheus, Grafana, distributed tracing, structured logging, metrics, alerting, dashboards, or monitoring. Activates for observability development tasks.