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benchmarking-kubernetes-with-kube-bench

Installs and runs the kube-bench tool against a Kubernetes cluster as a Job, DaemonSet, or standalone binary, selecting the correct benchmark version and targets (control plane, etcd, kubelet, worker nodes) and emitting JSON or JUnit output for pipelines. Use when setting kube-bench up for the first time, choosing which benchmark version and node targets to run, wiring it into CI, or troubleshooting skipped or misdetected checks. Keywords: kube-bench, DaemonSet, --benchmark, --targets, JSON output, JUnit, CI integration. Do not use for interpreting the findings or producing an audit report - use performing-kubernetes-cis-benchmark-with-kube-bench.

Quality

93%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

85%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 content is highly actionable with a clear, validated workflow and executable commands throughout, only slightly padded in the overview. Its main gap is progressive disclosure: real bundle files (api-reference.md, standards.md, agent.py) are not referenced or linked from the body, leaving their existence undiscoverable.

Suggestions

Link the bundle files from the body: add pointers such as "See [references/api-reference.md](references/api-reference.md) for the full command/flag table" under the relevant workflow steps and a Tools/Resources note for scripts/agent.py.

Trim the Overview's background on what kube-bench/CIS Benchmark is and the extended T1610 explanation, which restates knowledge Claude already has.

Move the command/flag detail duplicated between the body and references/api-reference.md out of the body, keeping only the most-used flags inline and pointing to the reference for the rest.

DimensionReasoningScore

Conciseness

The body is largely efficient with executable commands throughout, but the Overview paragraph explains concepts Claude largely already knows (what kube-bench is, the CIS Benchmark, and T1610 exposition) that could be trimmed; this is a minor instance of over-explanation rather than severe padding, so it sits just above the midpoint at 4 rather than 5.

4 / 5

Actionability

Provides fully executable, copy-paste-ready commands for install, in-cluster Job, target selection, benchmark pinning, per-check run/skip, JSON/JUnit/ASFF output, concrete remediation edits, and re-validation, covering the common cases — matching the score-5 anchor.

5 / 5

Workflow Clarity

An explicit 8-step numbered workflow sequences the work end-to-end, with step 8 providing a re-run validation feedback loop after remediation and a Validation Criteria checklist confirming each phase; this satisfies the explicit-validation-checkpoints anchor.

5 / 5

Progressive Disclosure

The body is well-sectioned and bundle files exist (references/api-reference.md, references/standards.md, scripts/agent.py), but the body never signals or links to them, so references are present yet not clearly surfaced; this fits the score-3 anchor (structure present but references not clearly signaled) rather than 4 (references mostly clear).

3 / 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.

The description is excellent: it concisely states concrete capabilities, provides natural trigger terms plus a keyword list, and clearly demarcates when to use it versus a sibling skill. It fully satisfies the what/when/distinctiveness criteria.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — "Installs and runs the kube-bench tool ... as a Job, DaemonSet, or standalone binary", "selecting the correct benchmark version and targets", "emitting JSON or JUnit output" — with comprehensive coverage, matching the score-5 anchor.

5 / 5

Completeness

Explicitly answers both "what" (install/run kube-bench, select version/targets, emit JSON/JUnit) and "when" ("Use when setting kube-bench up for the first time, choosing which benchmark version and node targets to run, wiring it into CI, or troubleshooting") with concrete trigger phrases, matching the score-5 anchor.

5 / 5

Trigger Term Quality

Includes natural phrases users would say ("setting kube-bench up for the first time", "wiring it into CI", "troubleshooting skipped or misdetected checks") plus an explicit "Keywords:" list (kube-bench, DaemonSet, --benchmark, --targets, JSON output, JUnit, CI integration), giving comprehensive coverage with synonyms.

5 / 5

Distinctiveness Conflict Risk

Occupies a clear niche (running/scoping kube-bench) and adds an explicit exclusion boundary ("Do not use for interpreting the findings ... use performing-kubernetes-cis-benchmark-with-kube-bench"), minimizing conflict risk.

5 / 5

Total

20

/

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

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository

mukul975/Anthropic-Cybersecurity-Skills

Path

skills/benchmarking-kubernetes-with-kube-bench/SKILL.md

Reviewed

Table of Contents

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