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Skillskubesense-skills

kubesense-skills

Deployment & operations

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Updated 10-16 06:22

About this skill

KubeSense Skills

Observability skills for KubeSense, grouped by telemetry surface.

Skills

Skill Description
kubesense-mcp The tool layer — connection, auth, the full tool inventory, the WHERE/field-name contract every query skill inherits. Start here.
kubesense-logs Search and aggregate logs
kubesense-traces Spans, latency percentiles, error rates, distributed-trace waterfalls
kubesense-metrics PromQL/MetricsQL over Kubernetes, infrastructure and cloud-provider (AWS/GCP/Azure/Atlas/Confluent/Kong) metrics
kubesense-spl SPL — the piped log query language, and its inverted field contract
kubesense-sql Raw ClickHouse SQL over logs and traces — joins, CTEs, window functions
kubesense-infra Cluster inventory: clusters, nodes, pods, workloads, infra failures, recent deploys
kubesense-alerts List, investigate, and create alert rules; generate import JSON; migrate Datadog monitors
kubesense-dashboards Create dashboards, or generate preset JSON to import
kubesense-slo Create and manage service level objectives; error budgets, burn-rate alerting, Datadog SLO migration

Routing

The user is asking… Skill
"show me errors / grep the logs" kubesense-logs
"p99 latency / slow requests / which service is failing" kubesense-traces
"CPU, memory, disk, saturation, capacity" kubesense-metrics
"how is my RDS / EC2 / Azure VM / Atlas cluster doing" kubesense-metrics
"what's running / why is this pod restarting / what changed" kubesense-infra
"alert me when… / what's firing / convert this Datadog monitor" kubesense-alerts
"build me a dashboard" kubesense-dashboards
"track an SLO / error budget / port a Datadog SLO" kubesense-slo
a query needing a join, CTE, or window function kubesense-sql
"search the logs with a pipeline" / SPL tab kubesense-spl
"this alert fired — what's wrong?" kubesense-alerts, then kubesense-infra
a tool returned a field-name or WHERE error kubesense-mcp

Prerequisites

Every skill except kubesense-dashboards, kubesense-alerts and kubesense-slo (which can generate JSON offline) needs the KubeSense MCP server connected. Those three need it for their validate and create tools, and for discovering real metric and field names — offline they fall back to JSON built from names the user supplies. kubesense-slo has no MCP tools at all: it drives the REST API directly, and uses the MCP query tools only to discover names.

The MCP server is served by kubeapi at /mcp over Streamable HTTP and authenticates with the same credentials as the REST API.

claude mcp add --scope user --transport http kubesense \
  https://<your-kubesense-host>/mcp \
  --header "x-api-key: <your-api-key>"

See kubesense-mcp for auth alternatives and verification.

Two Rules That Apply Everywhere

  1. Discover before querying. Never invent a field name, metric name, cluster name, or label. Every surface has a discovery tool; a guessed name either errors or — worse — returns an empty result that reads like "zero".

  2. Field names are catalog labels, not storage columns. Logs/traces queries take type, instance, service, method, status_code — not level, pod_name, app_service, subtype, return_code. The alert engine accepts a different set. See kubesense-mcp for the contract and kubesense-alerts for the divergence.

Install

npx skills add kubesense-ai/kubesense-mcp-skills --full-depth -y

[!IMPORTANT] --full-depth is required. Without it the installer stops at this root SKILL.md and installs only this index — 1 skill instead of 11 — with no warning.

If you are reading this as the only installed KubeSense skill, that is what happened. Re-run the command above with --full-depth.

Verify with npx skills add kubesense-ai/kubesense-mcp-skills --list --full-depth, which should report 11 skills. Or pick individual skills — see the README.