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Get Started Free →Multi-cluster Kubernetes dashboard with AI-powered operations via MCP server and 10+ built-in agent skills
.claude/skills/kubestellar-console/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-05 | ✗→✓ | ▲ Improved | 26% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 18% | 0% |
| case-07 | ✗→✓ | ▲ Improved | -10% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 5% | 0% |
| case-10 | ✗→✓ | ▲ Improved | -24% | 0% |
KubeStellar Console is an open-source multi-cluster Kubernetes dashboard (CNCF project) with AI-powered operations. It ships with kc-agent, an MCP server that bridges coding agents to kubeconfig and Kubernetes APIs, plus 10+ built-in agent skills for development, testing, and operations.
bashbrew tap kubestellar/tap && brew install kc-agent
bashkc-agent
This bridges the active kubeconfig context to any MCP-compatible coding agent. Do not start it from a cluster-admin or write-capable context unless the user explicitly accepts that risk.
The project ships with agent skills accessible via CLAUDE.md and AGENTS.md:
kc-agent bridges your active kubeconfig context to MCP-compatible agents. If that context carries cluster-admin, write permissions, or secret read access, agents inherit those capabilities.kc-agent uses. Start kc-agent only after switching KUBECONFIG/context to dedicated least-privilege credentials and verifying them.resources='*', because it includes sensitive objects such as Secrets. Prefer an explicit non-secret resource list and verify access before starting the MCP server:bash kubectl create serviceaccount kc-agent -n default kubectl create clusterrole kc-agent-readonly \ --verb=get,list,watch \ --resource=pods,services,deployments.apps,replicasets.apps,statefulsets.apps,daemonsets.apps,namespaces,nodes,events,configmaps kubectl create clusterrolebinding kc-agent-readonly \ --clusterrole=kc-agent-readonly \ --serviceaccount=default:kc-agent kubectl auth can-i get secrets --as=system:serviceaccount:default:kc-agent kubectl auth can-i list pods --as=system:serviceaccount:default:kc-agent
can-i command must return no; the second should return yes. Then create or select a kubeconfig that actually authenticates as that ServiceAccount before running kc-agent.kc-agent on a public network without authentication.kc-agent) installed separately via Homebrew.| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-05 | fail→pass | 4,842 | 1,602 | -67% | 1 | 1 | 0% | 990 | 1,247 | +26% | 0 | 0 | — |
case-06 | fail→pass | 6,880 | 6,125 | -11% | 1 | 1 | 0% | 1,363 | 1,612 | +18% | 0 | 0 | — |
case-07 | fail→pass | 8,740 | 1,778 | -80% | 1 | 1 | 0% | 1,529 | 1,380 | -10% | 0 | 0 | — |
case-08 | pass→pass | 8,568 | 2,233 | -74% | 1 | 1 | 0% | 1,395 | 1,311 | -6% | 0 | 0 | — |
case-09 | fail→pass | 8,475 | 2,126 | -75% | 1 | 1 | 0% | 1,355 | 1,427 | +5% | 0 | 0 | — |
case-10 | fail→pass | 9,971 | 1,588 | -84% | 1 | 1 | 0% | 1,694 | 1,284 | -24% | 0 | 0 | — |
case-11 | fail→pass | 7,103 | 4,271 | -40% | 1 | 1 | 0% | 1,336 | 1,872 | +40% | 0 | 0 | — |
case-12 | fail→pass | 9,453 | 2,230 | -76% | 1 | 1 | 0% | 1,516 | 1,393 | -8% | 0 | 0 | — |
case-13 | fail→pass | 6,720 | 1,649 | -75% | 1 | 1 | 0% | 1,068 | 1,244 | +16% | 0 | 0 | — |
case-14 | fail→pass | 7,390 | 2,750 | -63% | 1 | 1 | 0% | 1,190 | 1,361 | +14% | 0 | 0 | — |
case-15 | fail→fail | 13,003 | 4,469 | -66% | 1 | 1 | 0% | 2,644 | 1,973 | -25% | 0 | 0 | — |
case-16 | fail→fail | 9,985 | 5,740 | -43% | 1 | 1 | 0% | 1,653 | 1,973 | +19% | 0 | 0 | — |
case-17 | fail→pass | 11,019 | 1,866 | -83% | 1 | 1 | 0% | 1,862 | 1,367 | -27% | 0 | 0 | — |
case-18 | fail→fail | 8,685 | 6,200 | -29% | 1 | 1 | 0% | 1,660 | 2,164 | +30% | 0 | 0 | — |
case-19 | fail→fail | 5,639 | 2,961 | -47% | 1 | 1 | 0% | 933 | 1,551 | +66% | 0 | 0 | — |
case-20 | fail→fail | 12,487 | 5,396 | -57% | 1 | 1 | 0% | 2,456 | 2,092 | -15% | 0 | 0 | — |
case-21 | fail→pass | 5,512 | 1,925 | -65% | 1 | 1 | 0% | 925 | 1,411 | +53% | 0 | 0 | — |
case-22 | fail→fail | 7,611 | 2,211 | -71% | 1 | 1 | 0% | 1,511 | 1,431 | -5% | 0 | 0 | — |
case-01 | fail→pass | 21,996 | 8,270 | -62% | 1 | 1 | 0% | 3,181 | 2,700 | -15% | 0 | 0 | — |
case-02 | fail→pass | 14,485 | 8,429 | -42% | 1 | 1 | 0% | 2,435 | 2,624 | +8% | 0 | 0 | — |
case-03 | fail→pass | 17,233 | 8,251 | -52% | 1 | 1 | 0% | 3,149 | 2,750 | -13% | 0 | 0 | — |
case-04 | fail→pass | 7,522 | 2,106 | -72% | 1 | 1 | 0% | 1,372 | 1,377 | +0% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +68 percentage points is the difference between those two pass rates over the 22 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.