Query Prometheus metrics with PromQL for root-cause analysis workflows.
npx skills add https://github.com/zhanbei1/OpenOcta --skill prometheus-analysis
Please help me install this Agent Skill. Skill: Prometheus Analysis Source: https://github.com/zhanbei1/OpenOcta/tree/main/src/skills/prometheus-analysis Command: npx skills add https://github.com/zhanbei1/OpenOcta --skill prometheus-analysis
This skill provides guidance for querying Prometheus metrics, interpreting alert data, and using metrics for root cause analysis in order to drive remediation and reduce MTTR.
Run a quick RCA check by querying the current error rate for your service and inspecting latency trends over the last 5 minutes.
High-intent search queries and answers about installing and using this skill.
To analyze Prometheus metrics for root cause analysis, query error rates and latency trends using PromQL to identify the responsible service and bottleneck. This approach guides you to fetch relevant time-series data to support remediation and reduce Mean Time To Resolution (MTTR).
Investigating fired alerts and anomaly patterns involves querying Prometheus metrics with PromQL and interpreting time-series data to correlate metrics across services. This method helps derive actionable insights from the alert data to pinpoint the underlying incident.
Yes, you can use PromQL queries to fetch current error rates and inspect latency trends over specific time windows, such as the last 5 minutes. Interpreting these time-series metrics allows you to pinpoint the responsible service and bottleneck.
Correlating time-series metrics across services supports incident remediation by revealing anomaly patterns and performance issues across the monitored environment. This RCA-guided workflow derives actionable insights from Prometheus data to drive targeted fixes.
Analyzing Prometheus alert data requires a Prometheus monitoring environment tracking your services' metrics. You can then run a quick RCA check by querying current error rates and inspecting latency trends over the last 5 minutes using PromQL.
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