SKILLEMALL.ai

CF alibabacloud-lingjun-node-ops

Manage Alibaba Cloud Lingjun (hyper-)node full-lifecycle ops: stop/reboot/reimage, subscription renewal (bssopenapi renew-instance), spec change (change-node-types), repair (report-node-status/approve-operation), run-command, node-group default update, tag/untag, change-resource-group, plus read-only inventory prerequisite (list-cluster-nodes/describe-node/list-cluster-hyper-nodes). change-node-group & send-file out of scope (see alibabacloud-lingjun-cluster-scaling). Triggers: "lingjun node ops", "灵骏节点运维", "节点停机", "stop nodes", "节点重启", "reboot nodes", "节点重装", "reimage nodes", "节点续费", "renew lingjun node", "节点规格修改", "change node types", "节点维修", "report node status", "节点执行命令", "run command", "节点分组更新", "update node group", "节点打标", "tag node", "节点资源转组", "change node resource group".

Not recommendedcritical or high security findings
ClawHub Agent Skills author: alibabacloud-skills-team v0.0.1 MIT-0 29 files body ≈ 9 863 tokens Open the sourceclawhub.ai analyzed 2 d ago

Manage Alibaba Cloud Lingjun (hyper-)node full-lifecycle ops: stop/reboot/reimage, subscription renewal (bssopenapi renew-instance), spec change…

As a process F 48/100 · Will not run — References files that are not bundled: tests/

ProcedureData and analyticsInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
C
66/100
safety, quality, tests
Safety 60%
64
Quality 40%
69
Run on models
none yet
Process rating
F
48/100
Will not run
References files that are not bundled: tests/
Tools and files w 18
0
Result and completion w 14
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

What is at stake

The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.

Dangerous commands
If you install

The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.

For the author

Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.

How to improve

  1. Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
  2. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  3. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
  4. The text references files that are not there: add them or drop the references.
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 · 2

  • high Dangerous commands cmd-pipe-to-shell references/cli-installation-guide.md:22
    Downloads and executes remote code from an unrecognised host (pipe to shell)
    curl -fsSL --connect-timeout 10 --max-time 120 https://aliyuncli.alicdn.com/setup.sh | bash
  • high Dangerous commands cmd-pipe-to-shell SKILL.md:48
    Downloads and executes remote code from an unrecognised host (pipe to shell)
    curl -fsSL --connect-timeout 10 --max-time 120 https://aliyuncli.alicdn.com/setup.sh | bash

Files scanned: 29. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning body-long SKILL.md body ≈ 9863 tokens (recommended < 5000); move details to references/
  • warning missing-ref reference to a missing file: tests/

Process rating: all ten parameters 48/100

Will not run. References files that are not bundled: tests/
  • 0Tools and files. 1 referenced file(s) missing: tests/
  • 0Result and completion. Does not say what the result is
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 6 mutating operations with no state check
  • 40Execution cost. Instruction body is 9863 tokens: crowds the task out of the window
  • 50When it triggers. No condition that starts the skill
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 85Steps. 39 steps, 1 vague phrases
  • 100Failures and branches. 2 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
  • low The response is described with custom markup (27 tags): a typed call is more reliable

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

  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 16 example trigger phrases
  • +4Description says when NOT to use the skill
  • +3Description length 790: enough signal without eating the budget
  • +4Structure: 28 headings
  • +3Step-by-step instructions: 39 items
  • +4Has examples (14 code blocks)
  • +4Reference files are cited in the instructions (16 of 16)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.

External checks

ClawHub: suspicious
The skill is a real Alibaba Cloud infrastructure-operations guide, but its install path and missing safety wrapper implementation create review-worthy risk.
LLM: suspicious (high) · 8 Sept 2026