SKILLEMALL.ai

AA huawei-cloud-cce-alarm-correlation-engine

Huawei Cloud AOM alarm correlation and alarm-rule management skill for CCE operations. Use this skill when the user wants to: (1) query AOM active and historical alarms, (2) analyze alarm deduplication, alarm storms, severity grouping, burst alarms, and chronic alarms, (3) inspect CCE cluster alarm health, (4) query, create, update, delete, enable, or disable AOM alarm rules, (5) query or create notification action rules, (6) batch configure or clean CCE recommended AOM alarm rules from the cloud-side CCE alarm template. Trigger: user mentions "alarm correlation", "AOM alarm", "alarm rule", "alarm storm", "alarm inspection", "notification rule", "告警关联", "AOM 告警", "告警规则", "告警风暴", "通知规则", or "CCE 告警".

ClawHub Agent Skills author: huaweicloud-skills-team v1.0.0 MIT-0 18 files body ≈ 5 558 tokens Open the sourceclawhub.ai analyzed 2 d ago

As a process A 81/100 · Runs to the end — weak spots: running it twice, progress reporting

AnalyzerInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
88
Run on models
none yet
Process rating
A
81/100
Runs to the end
Progress reporting w 2
0
Running it twice w 4
30
Tools and files w 18
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 · 0

✓ No critical or high findings

Files scanned: 18. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 5558 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "id"

Process rating: all ten parameters 81/100

  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 33 mutating operations with no state check
  • 60Tools and files. Uses tools (bash, web, python, node) that frontmatter does not declare
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 70Execution cost. Instruction body is 5558 tokens
  • 85Steps. 79 steps, 1 vague phrases
  • 100Result and completion. Output format and completion criterion are stated
  • 100When it triggers. States when to use and when not to
  • 100Failures and branches. 1 branches, has a failure section
  • 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 14 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 708: enough signal without eating the budget
  • +4Structure: 29 headings
  • +3Step-by-step instructions: 79 items
  • +3Output format is stated explicitly
  • +4Has examples (14 code blocks)
  • +4Reference files are cited in the instructions (10 of 10)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.

External checks

ClawHub: clean
This is a disclosed Huawei Cloud alarm-management skill that can change monitoring rules only through a preview-and-confirm workflow.
LLM: benign (high) · VirusTotal: · 21 Jul 2026