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AB huawei-cloud-cce-pod-failure-diagnoser

Huawei Cloud CCE Pod failure diagnosis skill using Python SDK dispatcher. Use this skill when the user wants to: (1) diagnose Pod CrashLoopBackOff, ImagePullBackOff, OOMKilled, Pending, Evicted failures, (2) analyze Pod restart storms, (3) check Pod logs and events, (4) view Pod metrics and resource usage. Trigger: user mentions "Pod failure", "Pod 故障", "CrashLoopBackOff", "ImagePullBackOff", "OOMKilled", "Pod Pending", "Pod Evicted", "Pod 重启", "容器异常", "Pod 诊断", "Pod crash", "Pod 无法启动", "Pod 状态异常"

ClawHub Agent Skills author: shijingcheng v0.1.0 MIT-0 53 files body ≈ 3 439 tokens Open the sourceclawhub.ai↗ analyzed 21 h ago

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

As a process B 69/100 · Nearly there — weak spots: failures and branches, running it twice

IntegrationKubernetesData 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
93/100
safety, quality, tests
Safety 60%
93
Quality 40%
92
Run on models
none yet
Process rating
B
69/100
Nearly there
Failures and branches w 10
0
Running it twice w 4
30
Tools and files w 18
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 · 7

    ✓ No critical or high findings

    Medium and low: 7
    • 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(
    • low Secrets in code secret-password-literal scripts/huawei_cloud/cce_node.py:646
      Hard-coded password / key literal (may be an example)
      login = Login(user_password=UserPassword(username="root", password=salted_b64))
    • low Secrets in code secret-password-literal scripts/huawei_cloud/cce_nodepool.py:456
      Hard-coded password / key literal (may be an example)
      user_password.password = base…ode(hashed.encode("utf-8")).decode("utf-8")

    Files scanned: 53. 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 69/100

    • 0Failures and branches. Linear process with no failure handling
    • 30Running it twice. 5 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
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 85Steps. 64 steps, 1 vague phrases
    • 100When it triggers. States when to use and when not to
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3439 tokens
    • 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
    • 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)
    • -31 of 2 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 12 example trigger phrases
    • +3Description length 502: enough signal without eating the budget
    • +4Structure: 20 headings
    • +3Step-by-step instructions: 64 items
    • +3Output format is stated explicitly
    • +4Has examples (4 code blocks)
    • +4Reference files are cited in the instructions (3 of 5)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 92.

    External checks

    ClawHub: suspicious
    This pod-diagnosis skill has a coherent troubleshooting purpose, but it also ships and exposes many extra privileged cloud and Kubernetes actions that can reveal credentials/secrets or change infrastructure.
    LLM: suspicious (high) · VirusTotal: · 17 Jun 2026