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AB huawei-cloud-flexus-l-server-flexusagent-deployment

Deploy AI Agent development platform (Dify) on Huawei Cloud Flexus L instance, providing deployment operations, password management, MaaS model configuration, and workflow import capabilities. Trigger keywords: deploy flexusagent/一键部署Flexus AI Agent开发平台、change password/修改开发平台管理员密码、change dify password/修改dify平台密码、add maas provider/添加MaaS模型供应商、configure maas model/配置MaaS模型、view workflow/查看AI Agent工作流、import workflow/导入AI Agent工作流

ClawHub Agent Skills author: huaweiclouddev-dev v1.0.0 MIT-0 14 files body ≈ 4 118 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process B 70/100 · Nearly there — weak spots: when it triggers, running it twice

ProcedureInfrastructureAI and agentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
88
Quality 40%
94
Run on models
none yet
Process rating
B
70/100
Nearly there
When it triggers w 12
20
Running it twice w 4
30
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

    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 · 4

    ✓ No critical or high findings

    Medium and low: 4
    • medium Dangerous commands cmd-pipe-to-shell scripts/lib.py:986
      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/agent/reset_admin_password.sh | bash -s "${adminPassword}"'''
      code literal
    • medium Dangerous commands cmd-pipe-to-shell scripts/lib.py:993
      Downloads and executes remote code from an unrecognised host (pipe to shell) (string literal in code, not executed)
      "content": '''curl -sSL https://flex…com/stable/dify/scripts/import_yml_to_dify.sh | bash -s ${base…ing} ${dify_admin_password}'''
      code literal
    • low Exfiltration net-credential-use scripts/lib.py:986
      Credential used in a network call (verify the destination is the intended service) (quoted — discussed, not commanded)
      curl -sSL https://docu…com/solution-as-code-publicbucket/solution-as-code-moudle/agent/reset_admin_password.sh | bash -s "${adminPassword}"'''
      quoted
    • low Exfiltration net-credential-use scripts/lib.py:993
      Credential used in a network call (verify the destination is the intended service) (quoted — discussed, not commanded)
      "content": '''curl -sSL https://flex…com/stable/dify/scripts/import_yml_to_dify.sh | bash -s ${base…ing} ${dify_admin_password}'''
      quoted

    Files scanned: 14. 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 70/100

    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 34 mutating operations with no state check
    • 60Tools and files. Uses tools (bash, 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 4118 tokens
    • 100Steps. 59 steps
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 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

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +1No license
    • +2Single-language instructions
    • +3Description length 431: enough signal without eating the budget
    • +4Structure: 18 headings
    • +3Step-by-step instructions: 59 items
    • +3Output format is stated explicitly
    • +4Has examples (7 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)
    • +3All 9 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 94.

    External checks

    ClawHub: suspicious
    The skill mostly matches its cloud deployment purpose, but it exposes sensitive credentials/passwords and runs mutable remote scripts on cloud instances, so users should review it carefully before installing.
    LLM: suspicious (high) · 3 Aug 2026