SKILLEMALL.ai

AB ucs-cluster-onboarding-manager

Huawei Cloud UCS (Universal Cloud Service) cluster onboarding, lifecycle, and fleet grouping management skill using hcloud CLI. Use this skill when the user wants to: (1) register self-managed or CCE clusters to UCS - register/query/remove, (2) manage cluster lifecycle - update/query/list clusters, (3) manage fleet groups - create/delete/query cluster groups, (4) obtain cluster access information and kubeconfig, (5) download federation kubeconfig for multi-cluster access, (6) check UCS resource quotas. Trigger: user mentions "UCS cluster onboarding", "UCS 集群纳管", "UCS cluster registration", "UCS 注册集群", "UCS fleet", "UCS 舰队", "UCS 集群组", "cluster group", "fleet grouping", "UCS kubeconfig", "UCS 集群接入", "UCS federation", "UCS 联邦", "UCS 配额", "cluster lifecycle", "集群生命周期", "managed clusters", "纳管集群", "集群管理"

ClawHub Agent Skills author: shijingcheng v0.1.0 MIT-0 10 files body ≈ 5 824 tokens Open the sourceclawhub.ai analyzed 2 d ago

Huawei Cloud UCS (Universal Cloud Service) cluster onboarding, lifecycle, and fleet grouping management skill using hcloud CLI.

As a process B 73/100 · Nearly there — weak spots: progress reporting

IntegrationKubernetesSales and CRMInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
82
Run on models
none yet
Process rating
B
73/100
Nearly there
Progress reporting w 2
0
Failures and branches w 10
50
Tools and files w 18
60
the three weakest of ten parameters · all ten

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: 10. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 5824 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "id"

Process rating: all ten parameters 73/100

  • 0Progress reporting. Says nothing while it works
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 70Execution cost. Instruction body is 5824 tokens
  • 85Steps. 75 steps, 1 vague phrases
  • 100When it triggers. States when to use and when not to
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. Mutating operations check current state
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 10 top-level sections: this looks like several domains in one skill

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)
  • +3Description length 811: 120–800 characters recommended
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 17 example trigger phrases
  • +4Structure: 27 headings
  • +3Step-by-step instructions: 75 items
  • +3Output format is stated explicitly
  • +4Has examples (6 code blocks)
  • +4Reference files are cited in the instructions (8 of 8)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.

External checks

ClawHub: suspicious
This skill is not clearly malicious, but it should be reviewed because it handles powerful Huawei Cloud/Kubernetes credentials and destructive cluster-management actions.
LLM: suspicious (high) · 16 Jun 2026