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AB huawei-cloud-cce-metric-analyzer

Huawei Cloud CCE Metric analysis skill using Python SDK dispatcher. Use this skill when the user wants to: (1) query Pod/Node CPU/memory/disk metrics, (2) get resource usage TopN rankings, (3) query ECS/ELB/EIP/NAT cloud resource metrics, (4) aggregate cluster monitoring data with anomaly detection, (5) detect threshold-based resource anomalies. Trigger: user mentions "metric analysis", "指标分析", "CCE metrics", "CCE 指标", "AOM metrics", "AOM 指标", "resource metrics", "资源指标", "CPU usage", "CPU 使用率", "memory usage", "内存使用率", "performance monitoring", "性能监控", "TopN", "resource ranking", "资源排名"

ClawHub Agent Skills author: shijingcheng v0.1.1 MIT-0 52 files body ≈ 4 078 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

Huawei Cloud CCE Metric analysis skill using Python SDK dispatcher.

As a process B 67/100 · Nearly there — weak spots: failures and branches, running it twice, progress reporting

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
67/100
Nearly there
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

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: 52. 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 67/100

    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 2 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
    • 70Execution cost. Instruction body is 4078 tokens
    • 100Steps. 67 steps
    • 100When it triggers. States when to use and when not to
    • 100Consistency. Name and required fields are in place
    • 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)
    • -31 of 2 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 11 example trigger phrases
    • +3Description length 593: enough signal without eating the budget
    • +4Structure: 28 headings
    • +3Step-by-step instructions: 67 items
    • +3Output format is stated explicitly
    • +4Has examples (5 code blocks)
    • +4Reference files are cited in the instructions (3 of 3)

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

    External checks

    ClawHub: suspicious
    This skill presents itself as a read-only metrics analyzer, but its packaged dispatcher includes broad cluster administration, credential, secret, and mutation capabilities that are not disclosed in the skill description.
    LLM: suspicious (high) · VirusTotal: · 16 Jun 2026