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BF huawei-cloud-cce-availability-risk-scanner

Huawei Cloud CCE availability risk scanning skill using Python SDK dispatcher for read-only cluster risk assessment. Use this skill when the user wants to: (1) scan CCE clusters for availability risks including single replicas, missing PodDisruptionBudgets, unhealthy probes, unreasonable affinity or nodepool pinning, (2) assess master HA and utilization, node and workload AZ balance, gateway workload distribution, and core addon anti-affinity, (3) detect resource request/limit overcommit and capacity illusions, (4) produce risk-rated reports with remediation plans and YAML suggestions, (5) check control-plane visibility, node AZ distribution, nodepool distribution, and Pod spread. Trigger: user mentions "availability risk", "可用性风险", "availability scanner", "可用性扫描", "cluster inspection", "集群巡检", "risk assessment", "风险评估", "single point of failure", "单点故障", "availability gap", "可用性缺口", "PDB missing", "单副本", "AZ imbalance", "AZ 不均衡", "gateway concentration", "网关集中", "resource overcommit", "资源超配", "health probe missing", "探针缺失"

ClawHub Agent Skills author: shijingcheng v0.1.3 MIT-0 52 files body ≈ 4 484 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

Huawei Cloud CCE availability risk scanning skill using Python SDK dispatcher for read-only cluster risk assessment.

As a process F 66/100 · Will not run — References files that are not bundled: references/verification-method.md, references/common-pitfalls.md

IntegrationKubernetesAI and agentstype 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
B
79/100
safety, quality, tests
Safety 60%
95
Quality 40%
55
Run on models
none yet
Process rating
F
66/100
Will not run
References files that are not bundled: references/verification-method.md, references/common-pitfalls.md
Tools and files w 18
0
Failures and branches w 10
50
Result and completion w 14
60
the three weakest of ten parameters · all ten

How to improve

  1. Shorten the description to 1024 characters.
  2. The text references files that are not there: add them or drop the references.
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 · 5

✓ No critical or high findings

Medium and low: 5
  • low Secrets in code secret-high-entropy-token scripts/huawei_cloud/aom.py:665
    High-entropy token-like string (may be an id, hash or a credential)
    AddO…ody,
  • low Secrets in code secret-high-entropy-token scripts/huawei_cloud/aom.py:737
    High-entropy token-like string (may be an id, hash or a credential)
    body = AddO…ody(
  • low Secrets in code secret-high-entropy-token scripts/huawei_cloud/aom.py:932
    High-entropy token-like string (may be an id, hash or a credential)
    Dele…ody,
  • low Secrets in code secret-high-entropy-token scripts/huawei_cloud/aom.py:1336
    High-entropy token-like string (may be an id, hash or a credential)
    AddO…ody,
  • low Secrets in code secret-high-entropy-token scripts/huawei_cloud/aom.py:1372
    High-entropy token-like string (may be an id, hash or a credential)
    body = AddO…ody(

Files scanned: 25. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • error description-long description is 1040 chars, limit 1024
  • warning missing-ref reference to a missing file: references/verification-method.md
  • warning missing-ref reference to a missing file: references/common-pitfalls.md
  • note frontmatter-key unknown frontmatter key "id"

Process rating: all ten parameters 66/100

Will not run. References files that are not bundled: references/verification-method.md, references/common-pitfalls.md
  • 0Tools and files. 2 referenced file(s) missing: references/verification-method.md, references/common-pitfalls.md
  • 50Failures and branches. 0 branches, has a failure section
  • 60Result and completion. Output format stated, no completion criterion
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 70Execution cost. Instruction body is 4484 tokens
  • 100Steps. 49 steps
  • 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
  • 100Progress reporting. Reports progress
  • 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 1039: 120–800 characters recommended
  • -31 of 2 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 12 example trigger phrases
  • +4Structure: 20 headings
  • +3Step-by-step instructions: 49 items
  • +3Output format is stated explicitly
  • +4Has examples (4 code blocks)
  • +4Reference files are cited in the instructions (3 of 3)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 55.

External checks

ClawHub: suspicious
The skill is advertised as a read-only CCE availability scanner, but the packaged dispatcher exposes many live cloud and Kubernetes mutation actions that users would not expect from that purpose.
LLM: suspicious (high) · VirusTotal: · 16 Jun 2026