SKILLEMALL.ai

BC huawei-cloud-flexus-l-server-openclaw-deployment

Create Huawei Cloud Flexus L Instance (Lightweight Server), deploy OpenClaw application platform on it, and support installation and configuration of models and channels for deployed OpenClaw instances. Web UI access needs to be manually enabled in Huawei Cloud console. Trigger words: "Deploy OpenClaw", "Deploy Flexus L Instance", "Deploy Huawei Cloud Lightweight Server", "Model setting", "Channel Setting", "部署OpenClaw", "部署Flexus L实例", "部署华为云轻量服务器", "设置模型", "设置通道"

ClawHub Agent Skills author: huaweiclouddev-dev v1.0.0 MIT-0 13 files body ≈ 5 058 tokens Open the sourceclawhub.ai analyzed 4 d ago

As a process C 60/100 · Has gaps — weak spots: result and completion, when it triggers

GeneratorInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
B
76/100
safety, quality, tests
Safety 60%
75
Quality 40%
78
Run on models
none yet
Process rating
C
60/100
Has gaps
Result and completion w 14
0
When it triggers w 12
20
Tools and files w 18
60
the three weakest of ten parameters · all ten

The same skill appears in 1 more place: ClawHub

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Dangerous commands medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

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. 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 · 5

✓ No critical or high findings

Medium and low: 5
  • medium Dangerous commands cmd-pipe-to-shell scripts/lib.py:855
    Downloads and executes remote code from an unrecognised host (pipe to shell) (string literal in code, not executed)
    curl -sSL https://docu…com/solution-as-code-publicbucket/solution-as-code-moudle/deploying-clawdbot-agents/userdata/multi_model.sh | bash -s '${modelParam
    code literal
  • medium Dangerous commands cmd-pipe-to-shell scripts/lib.py:879
    Downloads and executes remote code from an unrecognised host (pipe to shell) (string literal in code, not executed)
    curl -sSL https://docu…com/solution-as-code-publicbucket/solution-as-code-moudle/deploying-clawdbot-agents/userdata/multi_channel.sh | bash -s '${channelL
    code literal
  • medium Dangerous commands cmd-pipe-to-shell scripts/lib.py:903
    Downloads and executes remote code from an unrecognised host (pipe to shell) (string literal in code, not executed)
    curl -sSL https://docu…com/solution-as-code-publicbucket/solution-as-code-moudle/deploying-clawdbot-agents/userdata/openclaw_gateway_manager.sh | bash -s 
    code literal
  • medium Dangerous commands cmd-pipe-to-shell scripts/lib.py:1098
    Downloads and executes remote code from an unrecognised host (pipe to shell) (string literal in code, not executed)
    install_command = f"curl -sSL https://docu…com/solution-as-code-publicbucket/solution-as-code-moudle/deploying-clawdbot-agents/userdata/multi_model.sh | b
    code literal
  • medium Dangerous commands cmd-pipe-to-shell scripts/lib.py:1184
    Downloads and executes remote code from an unrecognised host (pipe to shell) (string literal in code, not executed)
    install_command = f"curl -sSL https://docu…com/solution-as-code-publicbucket/solution-as-code-moudle/deploying-clawdbot-agents/userdata/multi_channel.sh |
    code literal

Files scanned: 13. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 5058 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 60/100

  • 0Result and completion. Does not say what the result is
  • 20When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
  • 60Failures and branches. 2 branches
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 70Execution cost. Instruction body is 5058 tokens
  • 100Steps. 72 steps
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

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)
  • +3Output format is not stated: the model decides each time
  • -42 reference files, but SKILL.md never points to them: the model will not open them
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 8 example trigger phrases
  • +3Description length 469: enough signal without eating the budget
  • +4Structure: 20 headings
  • +3Step-by-step instructions: 72 items
  • +4Has examples (11 code blocks)
  • +3All 8 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.

External checks

ClawHub: suspicious
The skill’s cloud deployment purpose is coherent, but it handles paid infrastructure, cloud credentials, secrets, and remote root execution with under-scoped controls and unsafe logging.
LLM: suspicious (high) · 3 Aug 2026