pantheon-ai/k8s-debug
Comprehensive Kubernetes debugging and troubleshooting toolkit. Use this skill when diagnosing Kubernetes cluster issues, debugging failing pods, investigating network connectivity problems, analyzing resource usage, troubleshooting deployments, or performing cluster health checks.
Overall
score
100%
Does it follow best practices?
Validation for skill structure
Discovery
100%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is an excellent skill description that follows best practices. It provides specific, concrete actions in third person voice, includes comprehensive trigger terms that users would naturally use, explicitly states both capabilities and usage conditions, and maintains clear distinctiveness through Kubernetes-specific terminology.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'Inspect pod logs', 'analyze resource quotas', 'trace network policies', 'check deployment rollout status', and 'run cluster health checks'. These are all distinct, actionable capabilities. | 3 / 3 |
Completeness | Clearly answers both what (inspect logs, analyze quotas, trace policies, check rollouts, run health checks) AND when with explicit 'Use this skill when...' clause covering multiple trigger scenarios (diagnosing issues, debugging pods, investigating network problems, etc.). | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural terms users would say: 'Kubernetes', 'pod logs', 'network policies', 'deployment', 'cluster health', 'failing pods', 'network connectivity', 'resource usage'. These match how users naturally describe K8s troubleshooting needs. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with Kubernetes-specific terminology throughout. The combination of 'pod logs', 'network policies', 'deployment rollout', and 'cluster health' creates a clear niche that wouldn't conflict with general DevOps or other container skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
100%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is an excellent skill that demonstrates best practices across all dimensions. It provides immediately actionable debugging patterns with executable commands, includes proper validation checkpoints for destructive operations, and maintains a clean structure that progressively discloses complexity through external references.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is lean and efficient, providing commands and patterns without explaining what Kubernetes is or how kubectl works. Every section delivers actionable content with minimal preamble. | 3 / 3 |
Actionability | All commands are fully executable with clear placeholders. The skill provides copy-paste ready bash commands with specific flags and options, plus concrete examples for each debugging scenario. | 3 / 3 |
Workflow Clarity | Emergency operations include explicit verification steps before and after each destructive action. The drain node workflow demonstrates excellent sequencing with cordon → drain → verify → uncordon pattern with validation at each step. | 3 / 3 |
Progressive Disclosure | Clear structure with quick patterns at top, detailed commands in middle, and references to external files (troubleshooting_workflow.md, common_issues.md) for deeper content. Navigation is one level deep and well-signaled. | 3 / 3 |
Total | 12 / 12 Passed |
Validation
100%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 11 / 11 Passed
Validation for skill structure
No warnings or errors.
Install with Tessl CLI
npx tessl i pantheon-ai/k8s-debugReviewed
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