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CF gke-basics

Plans, creates, and configures production-ready Google Kubernetes Engine (GKE) clusters using the golden path Autopilot configuration. Covers networking, security, observability, scaling, cost optimization, and AI/ML inference on GKE.

davila7/claude-code-templates Agent Skills author: davila7 MIT 27 files body ≈ 1 005 tokens Open the sourcegithub.com↗ analyzed 6 d ago

Plans, creates, and configures production-ready Google Kubernetes Engine (GKE) clusters using the golden path Autopilot configuration.

As a process F 33/100 · No process to follow — weak spots: steps, result and completion, when it triggers

GeneratorKubernetesInfrastructuretype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
C
88/100
safety, quality, tests
Safety 60%
98
Quality 40%
72
Run on models
none yet
Process rating
F
33/100
No process to follow
Steps w 15
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 2

✓ No critical or high findings

Medium and low: 2
  • low Risky intent intent-offensive-security references/gke-security.md:147
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (detector / deny-list definition)
    | `restricted` | **Golden path** | Production workloads -- blocks privilege escalation, host access, root |
    detector
  • low Risky intent intent-offensive-security references/gke-security.md:167
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (detector / deny-list definition)
    `restricted` blocks: running as root, privilege escalation, host networking/PID/IPC, host path volumes, and most capabilities. The golden path `workload-identity-pod.yaml` already complies.
    detector

Files scanned: 27. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "source"

Process rating: all ten parameters 33/100

  • 0Steps. Prose only: no discrete steps
  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 3 mutating operations with no state check
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (node) that frontmatter does not declare
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1005 tokens

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3No numbered steps or checklist
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +3Description length 234: enough signal without eating the budget
  • +4Structure: 3 headings
  • +4Has examples (1 code blocks)
  • +4Reference files are cited in the instructions (21 of 21)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.