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karpenter-autoscaling

Karpenter for intelligent Kubernetes node autoscaling on EKS. Use when configuring node provisioning, optimizing costs with Spot instances, replacing Cluster Autoscaler, implementing consolidation, or achieving 20-70% cost savings.

Quality

84%

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SecuritybySnyk

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SKILL.md
Quality
Evals
Security

Quality

Content

75%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 complete, executable YAML/HCL/bash for every workflow and a clear, well-signaled three-file reference bundle behind it. Its main weakness is token efficiency: duplicated Quick Start/Quick Reference sections, repeated cost-savings statistics, and inline dated version claims could all be consolidated, and a few workflows would benefit from explicit post-step verification commands.

Suggestions

Delete the trailing 'Quick Reference' section — its install command, basic NodePool, and monitor command all duplicate Quick Start content already shown in full.

State the cost-savings figures once (e.g., in Overview) and reference them elsewhere instead of repeating '20-70%' and the Real-World Results list across the When to Use, Workflow 3, and Quick Reference sections.

Move time-sensitive claims such as 'recommended autoscaler for production EKS workloads (2025)' and pinned chart versions into a short, clearly labeled version/compatibility note so they can be maintained in one place.

Add an explicit verification step to Workflows 2 and 5 (e.g., 'kubectl get nodes -l karpenter.sh/nodepool=flexible' after applying) to match the checklist-and-rollback rigor of the migration workflow.

DimensionReasoningScore

Conciseness

The bulk is executable, code-heavy material, but there is more than minor padding: the closing 'Quick Reference' section duplicates Quick Start content (install command, basic NodePool, monitor command), cost-savings percentages repeat four-plus times (Overview 'Real-World Results', When to Use, Workflow 3, Quick Reference), and dated/version claims ('recommended autoscaler for production EKS workloads (2025)', pinned chart version) appear inline rather than in a labeled compatibility note. This fits anchor 3 rather than 4's 'minor instances', and is far from anchor 2 since it never explains generic concepts Claude already knows.

3 / 5

Actionability

Fully copy-paste-ready throughout: complete Helm/Terraform install blocks, full NodePool/EC2NodeClass/PDB/GPU-pod YAML specs, and kubectl commands for deployment, verification, and log filtering. The examples cover the common cases (default pool, spot/on-demand split, consolidation, migration, GPU), matching anchor 5.

5 / 5

Workflow Clarity

All five workflows plus Quick Start are clearly sequenced with numbered steps, and the migration workflow adds a testing checklist and rollback plan; the Quick Start includes verification commands ('Verify nodes', watching controller logs). It falls short of anchor 5 because several workflows (e.g., spot mix, GPU provisioning) list steps without explicit verification checkpoints after execution.

4 / 5

Progressive Disclosure

The three referenced files (references/installation.md, nodepools.md, optimization.md) exist, are substantive, one level deep, and are well signaled both inline ('See: references/...') and in a dedicated Reference Documentation section with descriptions. Below anchor 5 because the body itself is heavy (~800 lines) and inlines full workflow YAML that partially duplicates what the reference guides cover, so the overview/reference split is good but not clean.

4 / 5

Total

16

/

20

Passed

Description

87%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 strong: it pairs a concrete statement of what the skill does with an explicit, comma-separated 'Use when' clause covering the main real-world triggers, all in third person. Minor room remains in keyword coverage (a few natural synonyms are absent) and full action enumeration.

DimensionReasoningScore

Specificity

Names the domain plus several concrete actions ('configuring node provisioning', 'optimizing costs with Spot instances', 'replacing Cluster Autoscaler', 'implementing consolidation'), matching the 'several specific actions with minor gaps' anchor. It falls short of 5 because coverage has minor gaps (no mention of installation, migration, or GPU provisioning) and 'intelligent' is a soft qualifier.

4 / 5

Completeness

Clearly answers both questions: the 'what' is 'Karpenter for intelligent Kubernetes node autoscaling on EKS' and the 'when' is an explicit 'Use when configuring node provisioning, optimizing costs with Spot instances, replacing Cluster Autoscaler, implementing consolidation, or achieving 20-70% cost savings' with concrete trigger phrases — a direct match for anchor 5.

5 / 5

Trigger Term Quality

Natural user phrasings are well covered: 'Kubernetes node autoscaling', 'EKS', 'Spot instances', 'Cluster Autoscaler', 'consolidation' — clearly above anchor 3's 'some relevant keywords'. A few natural variants users might say ('node groups', 'scale up', 'bin-packing') are missing, keeping it below 5.

4 / 5

Distinctiveness Conflict Risk

Clear niche (Karpenter on Amazon EKS) with distinct triggers ('replacing Cluster Autoscaler', 'Spot instances on EKS'); no meaningful overlap with other skills' triggers — anchor 5.

5 / 5

Total

18

/

20

Passed

Validation

93%

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

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (812 lines); consider splitting into references/ and linking

Warning

Total

15

/

16

Passed

Repository

fernandezbaptiste/Skrillz

Path

skills/karpenter-autoscaling/SKILL.md

Reviewed

Table of Contents

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