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CD kubernetes-gitops-platform-expert

Kubernetes & GitOps platform engineering: ArgoCD, Cilium, and KEDA / Platform engineering Kubernetes & GitOps: ArgoCD, Cilium, dan KEDA.

The skillemall take

Promises guidance on Production Kubernetes and GitOps: ArgoCD ApplicationSets, Cilium eBPF, KEDA, Gateway API, Pod Security Standards. One file with 2663 tokens, no scripts. Quality score 72, process 48, safety 100. No critical errors, linter clean.

This is reference material, not automation. No scripts included, no model runs. For platform engineers, it reads as documentation rather than a tool. Install if you need notes on these technologies; skip if you expect deployment automation.

roedyrustam/vibes-plug Agent Skills author: roedyrustam 1 file body ≈ 2 663 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Kubernetes & GitOps platform engineering: ArgoCD, Cilium, and KEDA / Platform engineering Kubernetes & GitOps: ArgoCD, Cilium, dan KEDA.

As a process D 48/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureKubernetesInfrastructuretype 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
89/100
safety, quality, tests
Safety 60%
100
Quality 40%
72
Run on models
none yet
Process rating
D
48/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
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 · 0

✓ No critical or high findings

Files scanned: 1. 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  • note edit-residue the text marks something as outdated (lines 13): check that old rules are not kept next to new ones — the full check reads the text for contradictions

Process rating: all ten parameters 48/100

  • 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. 4 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
  • 100Steps. 20 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2663 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)
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +3Description length 136: enough signal without eating the budget
  • +4Structure: 15 headings
  • +3Step-by-step instructions: 20 items
  • +4Has examples (3 code blocks)

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