SKILLEMALL.ai

AB aliyun-esa-manage

Use when managing Alibaba Cloud ESA — deploy HTML/static sites via Pages, manage Edge Routines (ER) for serverless edge functions, use Edge KV for distributed key-value storage, handle site management, DNS records, cache rules, and query traffic analytics via OpenAPI/SDK. Use when working with ESA, edge deployment, edge functions, Pages, ER, KV storage, DNS, cache, site configuration, traffic analytics, bandwidth trends, or top-N rankings.

ClawHub Agent Skills author: cinience v1.0.0 MIT-0 24 files body ≈ 4 119 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process B 68/100 · Nearly there — weak spots: when it triggers, running it twice, progress reporting

IntegrationInfrastructureData and analyticstype 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
B
68/100
Nearly there
Progress reporting w 2
0
When it triggers w 12
20
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 · 0

    ✓ No critical or high findings

    Files scanned: 24. 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 68/100

    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 45 mutating operations with no state check
    • 60Tools and files. Uses tools (python) 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 4119 tokens
    • 100Steps. 112 steps
    • 100Failures and branches. 5 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 13 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

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • -31 of 5 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +3Description length 443: enough signal without eating the budget
    • +4Structure: 49 headings
    • +3Step-by-step instructions: 112 items
    • +3Output format is stated explicitly
    • +4Has examples (13 code blocks)
    • +4Reference files are cited in the instructions (16 of 16)

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

    External checks

    ClawHub: suspicious
    This appears to be a legitimate Alibaba Cloud ESA management skill, but it gives an agent broad power to publish or delete live cloud resources and save analytics without enough safeguards.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026