Name: dt-obs-kubernetes
Rating: 72.8 (1 reviews)
Author: Dynatrace

dt-obs-kubernetes

Kubernetes cluster, pod, node, and workload monitoring. Use when analyzing K8s health, resource optimization, pod failures, OOMKills, scheduling, or security posture. Also use for Kubernetes operational events like pod restarts, OOM events, evictions, and cluster event history. Trigger: "Kubernetes pods", "K8s cluster health", "OOMKill", "pod restarts", "container CPU", "namespace resource usage", "over-provisioned pods", "privileged containers", "pod placement", "K8s node capacity", "running containers by cluster", "workload scheduling", "pod evictions", "K8s labels and annotations", "kubernetes events", "pod restart events", "OOM events", "K8s event history". Do NOT use for explaining existing queries, product documentation questions, AWS-specific resource queries, service-level RED metrics, distributed tracing, or log analysis — use the relevant skill instead.

Quality

89%

Does it follow best practices?

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Securityby

Passed

No findings from the security scan

Quality

Content

78%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A strong, actionable K8s monitoring skill built on executable DQL with excellent progressive disclosure via real bundle files. Its main weakness is conciseness: a few generic best-practices lists restate knowledge Claude already has.

Suggestions

Trim or remove the 'Monitoring Recommendations' and 'Configuration Standards' bullet lists — they restate basic Kubernetes hygiene Claude already knows and add tokens without new information.

Move any remaining generic K8s guidance into a reference file, keeping SKILL.md focused on Dynatrace-specific DQL syntax, field names, and entity traversal that Claude cannot derive.

Consider collapsing the two near-duplicate reference signposting blocks ('When to Load References' and 'References') into one to reduce redundancy.

Dimension	Reasoning	Score
Conciseness	The core is lean, Dynatrace-specific DQL that Claude does not know, but the 'Monitoring Recommendations' and 'Configuration Standards' sections restate basic K8s advice Claude already knows ('Set resource limits', 'Use specific image tags (avoid :latest)', 'Use labels for organization'), which is padding that could be trimmed.	3 / 5
Actionability	Dozens of copy-paste-ready DQL queries with specific field names, filters, and parse/expand patterns cover the common cases (cluster health, resource optimization, OOMKill/restart troubleshooting, security, scheduling, Davis problems).	5 / 5
Workflow Clarity	Numbered Common Workflows (1–6) and a 'Choosing the Right Data Source' decision table sequence the work, and event-based troubleshooting gives an explicit combine-both-approaches procedure (1. events tool, 2. timeseries metrics); minor checkpoint gaps remain but the skill is read-only so no destructive/batch cap applies.	4 / 5
Progressive Disclosure	A concise overview with well-signaled one-level-deep references: a 'When to Load References' section gives per-file load conditions and all 8 referenced bundle files (cluster-inventory, labels-annotations, pod-node-placement, pod-debugging, workload-health, pv-pvc, ingress, network-policies) exist and are listed in a final References index.	5 / 5
	Total	17 / 20 Passed

Description

100%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A well-crafted description: third-person voice, concrete capabilities, comprehensive trigger synonyms, and explicit use/don't-use boundary guidance. It cleanly satisfies the what/when/distinctiveness requirements.

Dimension	Reasoning	Score
Specificity	Names the domain and lists multiple concrete actions — 'analyzing K8s health, resource optimization, pod failures, OOMKills, scheduling, or security posture' plus operational events (pod restarts, OOM events, evictions, cluster event history) — giving comprehensive coverage.	5 / 5
Completeness	Explicitly answers both 'what' ('Kubernetes cluster, pod, node, and workload monitoring') and 'when' ('Use when analyzing... Also use for...') with concrete trigger phrases.	5 / 5
Trigger Term Quality	Comprehensive natural trigger terms including synonyms ('Kubernetes pods'/'K8s cluster health', 'OOMKill'/'OOM events') and specific operational phrases users would actually say, covering 18 distinct triggers.	5 / 5
Distinctiveness Conflict Risk	Clear K8s monitoring niche with an explicit 'Do NOT use for...' exclusion clause carving out AWS queries, RED metrics, tracing, and log analysis, minimizing conflict with sibling skills.	5 / 5
	Total	20 / 20 Passed

Validation

100%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: Dynatrace/dynatrace-for-ai
Path: skills/dt-obs-kubernetes/SKILL.md
Commit: 3287f9d

Reviewed: 5 days ago

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