kubernetes-patterns

Kubernetes workload patterns, resource management, RBAC, probes, autoscaling, ConfigMap/Secret handling, and kubectl debugging for production-grade deployments. Use when writing or reviewing Kubernetes manifests, or debugging probes, RBAC, autoscaling, or resource limits.

Quality

78%

Does it follow best practices?

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Securityby

High

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Fix and improve this skill with Tessl

tessl review fix ./skills/kubernetes-patterns/SKILL.md

Quality

Content

65%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.

The body is highly actionable with copy-paste YAML and kubectl commands, but it is a long monolithic file with redundancy and lacks validation checkpoints for the destructive/batch operations it documents, capping workflow clarity. Splitting sections into reference files and adding verification steps would lift the weaker dimensions.

Suggestions

Move the Deployment template, kubectl cheatsheet, and RBAC chain into separate files under ./references/ and link to them from SKILL.md so the overview stays lean and progressively disclosed.

Add explicit validation/verification checkpoints (e.g., 'kubectl apply --dry-run=server', 'kubectl rollout status', 'kubectl auth can-i') before destructive or batch operations like apply, rollback, and RBAC changes.

De-duplicate the probe and resource YAML that currently repeats across the Deployment, Probes, Resource, and Anti-patterns sections to reduce token cost.

Dimension	Reasoning	Score
Conciseness	Mostly efficient for a patterns skill, but the YAML payload is large and partly redundant — probe and resource snippets repeat across the Deployment template, Probes, Resource, and Anti-patterns sections — and inline comments restate well-known facts ('Never use :latest', '100m = 0.1 CPU').	3 / 5
Actionability	Provides fully executable, copy-paste-ready YAML and kubectl commands with concrete decision tables, covering the common deployment, RBAC, autoscaling, and debugging cases.	5 / 5
Workflow Clarity	Sections are mapped via a 1–10 'How It Works' index and a jump table, but the skill drives batch/destructive operations (kubectl apply, rolling updates, RBAC, namespace creation) with no validation/verification checkpoints, which the rubric caps at 3.	3 / 5
Progressive Disclosure	No bundle files exist; the entire ~750-line reference is inlined in SKILL.md with section headers and a jump table for structure, but content that would fit separate reference files (Deployment template, cheatsheet, RBAC chain) is not split out.	3 / 5
	Total	14 / 20 Passed

Description

92%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.

The description is comprehensive and specific, clearly stating both capabilities and concrete activation triggers with strong natural-language phrasing. Its only minor gap is missing a few synonymous trigger terms.

Dimension	Reasoning	Score
Specificity	Names multiple specific concrete capability areas — 'workload patterns, resource management, RBAC, probes, autoscaling, ConfigMap/Secret handling, and kubectl debugging' — giving comprehensive coverage rather than vague abstractions.	5 / 5
Completeness	Explicitly answers both 'what' (patterns, management, handling, debugging) and 'when' via a concrete 'Use when writing or reviewing Kubernetes manifests, or debugging...' clause.	5 / 5
Trigger Term Quality	Includes natural phrases users say ('writing or reviewing Kubernetes manifests', 'debugging probes', 'RBAC', 'autoscaling', 'resource limits'); a few synonyms like 'kubectl' or 'K8s YAML' are absent, so not a perfect 5.	4 / 5
Distinctiveness Conflict Risk	A clearly delineated Kubernetes-production niche with distinct triggers; minimal overlap with adjacent skills like docker-patterns or deployment-patterns.	5 / 5
	Total	19 / 20 Passed

Validation

87%

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 — 14 / 16 Passed

Validation for skill structure

Criteria	Description	Result
skill_md_line_count	SKILL.md is long (757 lines); consider splitting into references/ and linking	Warning
metadata_version	'metadata.version' is missing	Warning

	Total	14 / 16 Passed

Repository: affaan-m/ECC
Path: skills/kubernetes-patterns/SKILL.md
Commit: 06c5e11

Reviewed: 3 days ago

Table of Contents

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