Is performance profiling supported?

Yes, the skill includes integration with Pyroscope, allowing you to view and analyze CPU and memory profiles for your applications.

Why should I use this skill instead of kubectl logs?

This skill utilizes Loki, which aggregates logs across the entire cluster. It provides superior full-text search, label filtering, and long-term historical access that standard kubectl logs cannot offer.

Does this skill support manual PromQL or LogQL queries?

Yes, while the skill offers simplified compound workflows like 'investigate-logs', it also provides raw tool access for executing custom PromQL and LogQL queries.

Does the skill handle Grafana Incident and OnCall tasks?

Yes, it includes specialized tools for listing active incidents, creating new incident rooms, checking on-call schedules, and identifying who is currently on-call.

Can I modify Grafana dashboards using this skill?

Absolutely. You can search for existing dashboards, view panel queries, and perform targeted updates using JSON patch operations to modify panels or queries.

Grafana Observability Suite

Name: Grafana Observability Suite
Author: fzymgc-house

byfzymgc-house

Analytics & Monitoring

Provides unified access to Grafana, Loki, and Prometheus for monitoring, logging, and incident management in Kubernetes clusters.

About

This skill integrates the full Grafana observability stack directly into Claude Code, allowing developers to query Loki logs, Prometheus metrics, and manage Grafana dashboards without leaving the terminal. It simplifies complex operations by providing high-level workflows for investigating application errors, checking system health, and managing on-call incidents. By prioritizing aggregated Loki logs and Prometheus time-series data over raw Kubernetes API calls, it offers a more robust, searchable, and historical interface for cluster diagnostics and performance profiling.

Key Features

Real-time incident management and Sift AI-powered investigations
0 GitHub stars
Automated dashboard discovery and targeted panel updates
Advanced log analysis with Loki and LogQL pattern matching
Seamless OnCall schedule tracking and alerting configuration
Time-series metric exploration using Prometheus and PromQL

Use Cases

Troubleshooting application errors by searching aggregated cluster logs with specific patterns and time ranges
Monitoring system health and latency via Prometheus time-series queries and quick-status checks
Managing the end-to-end incident response lifecycle from alert detection to activity logging and on-call tracking

About

Key Features

Real-time incident management and Sift AI-powered investigations
0 GitHub stars
Automated dashboard discovery and targeted panel updates
Advanced log analysis with Loki and LogQL pattern matching
Seamless OnCall schedule tracking and alerting configuration
Time-series metric exploration using Prometheus and PromQL

Use Cases

Troubleshooting application errors by searching aggregated cluster logs with specific patterns and time ranges
Monitoring system health and latency via Prometheus time-series queries and quick-status checks
Managing the end-to-end incident response lifecycle from alert detection to activity logging and on-call tracking