SKILLEMALL.ai

AB huawei-cloud-ecs-alert

Automate batch creation and management of Huawei Cloud CES alarm rules for ECS instances using hcloud CLI v7.2.2+. Use this skill to: (1) batch create alarms with templates (web/database), (2) update SMN notifications, (3) query ECS metrics and alarm lists. Trigger: "ECS alert", "create alert", "list alarms", "CPU alert", "memory alert", "ECS monitoring", "监控告警", "创建告警", "ECS 监控", "告警规则", "查询告警"

ClawHub Agent Skills author: huaweiclouddev-dev v1.0.0 MIT-0 21 files · 10 scripts body ≈ 4 208 tokens Open the sourceclawhub.ai analyzed 15 h ago

Automate batch creation and management of Huawei Cloud CES alarm rules for ECS instances using hcloud CLI v7.2.2+. Use this skill to: (1) batch create alarms…

As a process B 69/100 · Nearly there — weak spots: when it triggers, progress reporting

ProcedureInfrastructureData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
91
Run on models
none yet
Process rating
B
69/100
Nearly there
Progress reporting w 2
0
When it triggers w 12
20
Failures and branches w 10
55
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: 21. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note edit-residue the text marks something as outdated (lines 132, 216, 248, 295, 344): check that old rules are not kept next to new ones — the full check reads the text for contradictions

    Process rating: all ten parameters 69/100

    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 55Failures and branches. 1 branches
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 70Execution cost. Instruction body is 4208 tokens
    • 85Steps. 49 steps, 2 vague phrases
    • 100Result and completion. Output format and completion criterion are stated
    • 100Consistency. Name and required fields are in place
    • 100Running it twice. Mutating operations check current state
    • low 16 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)
    • -218 emoji in the instructions: noise for the model
    • -32 of 11 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 7 example trigger phrases
    • +3Description length 398: enough signal without eating the budget
    • +4Structure: 26 headings
    • +3Step-by-step instructions: 49 items
    • +3Output format is stated explicitly
    • +4Has examples (10 code blocks)
    • +4Reference files are cited in the instructions (8 of 8)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.

    External checks

    ClawHub: suspicious
    The skill is mostly coherent Huawei Cloud alarm automation, but it combines broad cloud permissions, credential handling guidance, unverified install guidance, and alert-silencing notification actions that deserve manual review.
    LLM: suspicious (high) · 3 Aug 2026