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AB huawei-cloud-ucs-cluster-onboarding-manager

Huawei Cloud UCS cluster onboarding, lifecycle, and fleet management via hcloud CLI. Register/query/remove clusters, manage fleet groups, obtain kubeconfig, check quotas. Trigger: "UCS cluster onboarding", "UCS 集群纳管", "UCS fleet", "UCS 舰队", "UCS kubeconfig", "UCS federation", "UCS 联邦", "UCS 配额", "cluster lifecycle", "纳管集群", "集群管理"

ClawHub Agent Skills author: huaweicloud-skills-team v1.0.2 MIT-0 14 files body ≈ 6 580 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

Huawei Cloud UCS cluster onboarding, lifecycle, and fleet management via hcloud CLI.

As a process B 75/100 · Nearly there — weak spots: running it twice, progress reporting

ProcedureKubernetesInfrastructureSales and CRMtype and topics are labelled automatically from the skill text
Runs in: OpenClaw 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) Goose OpenCode
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
84
Run on models
none yet
Process rating
B
75/100
Nearly there
Progress reporting w 2
0
Running it twice w 4
30
When it triggers w 12
50
the three weakest of ten parameters · all ten

The same skill appears in 1 more place: ClawHub

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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: 14. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 6580 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 75/100

  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 37 mutating operations with no state check
  • 50When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 70Execution cost. Instruction body is 6580 tokens
  • 85Steps. 101 steps, 1 vague phrases
  • 100Result and completion. Output format and completion criterion are stated
  • 100Failures and branches. 2 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 12 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (4 tags): a typed call is more reliable

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

  • +4Description does not say when NOT to use the skill (false activations)
  • -212 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 9 example trigger phrases
  • +3Description length 332: enough signal without eating the budget
  • +4Structure: 25 headings
  • +3Step-by-step instructions: 101 items
  • +3Output format is stated explicitly
  • +4Has examples (10 code blocks)
  • +4Reference files are cited in the instructions (12 of 12)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.

External checks

ClawHub: suspicious
The skill is coherent for Huawei UCS cluster management, but it needs Review because it handles powerful cloud and Kubernetes credentials with some unsafe or under-scoped examples.
LLM: suspicious (high) · 26 Aug 2026