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BB huawei-cloud-cce-alarm-correlation-engine

Huawei Cloud AOM alarm correlation analysis skill for CCE operations. Use this skill when the user wants to: (1) query AOM active and historical alarms, (2) analyze alarm deduplication, severity grouping, and burst/steady alarm identification, (3) inspect CCE cluster alarm health, (4) manage AOM alarm rules (query, create, update, delete, enable, disable), (5) check AOM action rules and mute rules for notification gaps, (6) create event alarm rules referencing CCE event lists or Prometheus metric alarms. Trigger: user mentions "alarm correlation", "告警关联", "AOM alarm", "AOM 告警", "alarm deduplication", "告警去重", "alarm storm", "告警风暴", "alarm inspection", "告警巡检", "alarm rules", "告警规则"

ClawHub Agent Skills author: shijingcheng v0.1.3 MIT-0 54 files body ≈ 5 601 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

Huawei Cloud AOM alarm correlation analysis skill for CCE operations.

As a process B 75/100 · Nearly there — no weak spots found

AnalyzerSoftware developmentData and analyticsWriting and documentstype 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
88/100
safety, quality, tests
Safety 60%
93
Quality 40%
80
Run on models
none yet
Process rating
B
75/100
Nearly there
Failures and branches w 10
55
Tools and files w 18
60
Result and completion w 14
60
the three weakest of ten parameters · all ten

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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: 54. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 5601 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "id"

Process rating: all ten parameters 75/100

  • 55Failures and branches. 1 branches
  • 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 5601 tokens
  • 85Steps. 45 steps, 3 vague phrases
  • 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)
  • -220 emoji in the instructions: noise for the model
  • -31 of 2 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 7 example trigger phrases
  • +3Description length 688: enough signal without eating the budget
  • +4Structure: 28 headings
  • +3Step-by-step instructions: 45 items
  • +3Output format is stated explicitly
  • +4Has examples (7 code blocks)
  • +4Reference files are cited in the instructions (5 of 5)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.

External checks

ClawHub: suspicious
This skill is not clearly malware, but its packaged dispatcher exposes far broader Huawei Cloud and Kubernetes powers than the alarm-correlation description discloses.
LLM: suspicious (high) · VirusTotal: · 16 Jun 2026