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AB huawei-cloud-cce-node-failure-diagnoser

Huawei Cloud CCE Node failure diagnosis skill using Python SDK dispatcher. Use this skill when the user wants to: (1) diagnose CCE node NotReady, node resource pressure, node failure events, (2) analyze node disk/memory/CPU pressure, (3) check node status and conditions, (4) view node metrics and events. Trigger: user mentions "node failure", "节点故障", "NodeNotReady", "节点 NotReady", "node pressure", "节点压力", "node disk pressure", "磁盘压力", "node eviction", "节点驱逐", "节点异常", "节点诊断", "CCE node", "CCE 节点", "节点状态"

ClawHub Agent Skills author: shijingcheng v0.1.1 MIT-0 55 files body ≈ 3 101 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

Huawei Cloud CCE Node failure diagnosis skill using Python SDK dispatcher.

As a process B 79/100 · Nearly there — no weak spots found

IntegrationKubernetesSoftware developmentData and analyticstype 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
99/100
safety, quality, tests
Safety 60%
100
Quality 40%
98
Run on models
none yet
Process rating
B
79/100
Nearly there
Failures and branches w 10
50
Tools and files w 18
60
Result and completion w 14
60
the three weakest of ten parameters · all ten

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 · 0

    ✓ No critical or high findings

    Files scanned: 0. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "id"

    Process rating: all ten parameters 79/100

    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash, web, python, node) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 40 steps
    • 100When it triggers. States when to use and when not to
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3101 tokens
    • 100Running it twice. No mutating operations
    • 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)
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 8 example trigger phrases
    • +3Description length 508: enough signal without eating the budget
    • +4Structure: 23 headings
    • +3Step-by-step instructions: 40 items
    • +3Output format is stated explicitly
    • +4Has examples (3 code blocks)
    • +4Reference files are cited in the instructions (3 of 6)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 98.

    External checks

    ClawHub: suspicious
    The skill mostly fits Huawei Cloud CCE diagnosis, but it ships broad live cluster administration and credential-revealing capabilities under a read-only node-diagnosis description.
    LLM: suspicious (high) · VirusTotal: · 16 Jun 2026