SKILLEMALL.ai

AB alibabacloud-mse-nacos-inspection

Perform batch inspection on Alibaba Cloud MSE Nacos instances, checking configuration count usage, connection count usage, QPS usage, and TPS usage. Supports two modes: inspecting a specified list of instance IDs, or inspecting all Nacos instances in a region by region. Triggers when the user needs to perform health inspection, capacity assessment, or usage check on MSE Registration & Configuration Center instances, or when the user mentions "inspect nacos", "check nacos instance usage", "nacos capacity inspection", "MSE registry inspection", "nacos inspection", "check nacos config count", "nacos connection count over limit", "nacos QPS/TPS usage", "inspect all nacos in a region", "inspect nacos instances in cn-hangzhou", "check nacos usage across all regions"

ClawHub Agent Skills author: alibabacloud-skills-team v0.0.1 MIT-0 7 files body ≈ 4 468 tokens Open the sourceclawhub.ai analyzed 2 d ago

Perform batch inspection on Alibaba Cloud MSE Nacos instances, checking configuration count usage, connection count usage, QPS usage, and TPS usage.

As a process B 71/100 · Nearly there — weak spots: running it twice, progress reporting

ProcedureInfrastructureData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
98/100
safety, quality, tests
Safety 60%
100
Quality 40%
96
Run on models
none yet
Process rating
B
71/100
Nearly there
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

    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: 7. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 71/100

    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 2 mutating operations with no state check
    • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 70Execution cost. Instruction body is 4468 tokens
    • 85Steps. 53 steps, 3 vague phrases
    • 100Failures and branches. 2 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 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)
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 11 example trigger phrases
    • +3Description length 770: enough signal without eating the budget
    • +4Structure: 32 headings
    • +3Step-by-step instructions: 53 items
    • +3Output format is stated explicitly
    • +4Has examples (10 code blocks)
    • +4Reference files are cited in the instructions (5 of 5)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 96.

    External checks

    ClawHub: clean
    This skill is a disclosed Alibaba Cloud Nacos read-only inspection helper, with credential setup documentation users should handle carefully.
    LLM: benign (high) · VirusTotal: · 13 Jul 2026