SKILLEMALL.ai
GitHub ↗ RU Sign in

AC huawei-cloud-cce-cluster-management

Huawei Cloud CCE (Cloud Container Engine) cluster lifecycle management skill using Python SDK v3. Use this skill when the user wants to: (1) create, delete, hibernate, or awake CCE clusters, (2) list clusters and query cluster/node/nodepool/addon information, (3) manage node pools (create, delete, resize), (4) manage nodes (create, delete, cordon, uncordon, drain), (5) manage addons (install, uninstall, update), (6) bind/unbind cluster EIP for public access, (7) get cluster kubeconfig. Trigger: user mentions "CCE cluster", "create cluster", "delete cluster", "node pool", "node management", "hibernate cluster", "awake cluster", "addon", "kubeconfig", "EIP binding", "CCE 集群", "创建集群", "删除集群", "节点池", "节点管理", "休眠集群", "唤醒集群", "插件", "kubeconfig", "EIP 绑定"

ClawHub Agent Skills author: huaweiclouddev-dev v1.0.0 MIT-0 19 files body ≈ 2 657 tokens Open the sourceclawhub.ai↗ analyzed 21 h ago

Huawei Cloud CCE (Cloud Container Engine) cluster lifecycle management skill using Python SDK v3.

As a process C 63/100 · Has gaps — weak spots: failures and branches, running it twice, progress reporting

IntegrationSoftware developmenttype 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
98/100
safety, quality, tests
Safety 60%
98
Quality 40%
97
Run on models
none yet
Process rating
C
63/100
Has gaps
Failures and branches w 10
0
Progress reporting w 2
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

The same skill appears in 1 more place: ClawHub

How to improve

    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 Secrets in code secret-password-literal references/cce-api-guide.md:82
      Hard-coded password / key literal (may be an example)
      login.user_password = UserPassword(password=salted_b64)
    • low Secrets in code secret-password-literal references/cce-api-guide.md:118
      Hard-coded password / key literal (may be an example)
      login = Login(user_password=UserPassword(username="root", password=salted_b64))

    Files scanned: 19. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 63/100

    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 29 mutating operations with no state check
    • 60Tools and files. Uses tools (bash, web, python, node) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 85Steps. 40 steps, 1 vague phrases
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2657 tokens
    • low 10 top-level sections: this looks like several domains in one skill
    • low The response is described with custom markup (3 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)
    • -234 emoji in the instructions: noise for the model
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 12 example trigger phrases
    • +3Description length 758: enough signal without eating the budget
    • +4Structure: 24 headings
    • +3Step-by-step instructions: 40 items
    • +3Output format is stated explicitly
    • +4Has examples (3 code blocks)
    • +4Reference files are cited in the instructions (8 of 8)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 97.

    External checks

    ClawHub: suspicious
    The skill is a mostly coherent Huawei CCE administration tool, but it exposes sensitive cluster credentials and public-access changes with weaker warnings and controls than its own safety framing implies.
    LLM: suspicious (high) · VirusTotal: · 3 Aug 2026