AB huawei-cloud-flexus-l-deploy-jiuwenswarm
One-click deployment of JiuwenSwarm multi-Agent collaboration platform on Huawei Cloud Flexus L instances. Usage scenarios: When users need to quickly deploy JiuwenSwarm/JiuwenClaw on Huawei Cloud Flexus L instances, when they need to automatically create cloud instances and deploy AI Agent platforms, when they need to configure model APIs and message channels (Xiaoyi/Feishu/DingTalk). Automatically create instances, deploy applications via COC, configure models and message channels. Trigger keywords: JiuwenSwarm deployment, JiuwenClaw deployment, 九问Swarm部署, 九问Claw部署, 一键部署JiuwenSwarm, AI智能体平台部署, 部署九问Swarm, 部署九问Claw,云服务器部署AI平台.
One-click deployment of JiuwenSwarm multi-Agent collaboration platform on Huawei Cloud Flexus L instances.
As a process B 71/100 · Nearly there — weak spots: when it triggers, running it twice, progress reporting
How to improve
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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
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low Dangerous commands
cmd-background-processassets/deploy_script_template.sh:227Starts a background / autostarted processsystemctl enable jiuwenswarm
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low Exfiltration
read-dotenvreferences/troubleshooting.md:133Reads a .env filecat /opt/jiuwenswarm-env/jiuwenswarm/.env
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low Dangerous commands
cmd-pipe-to-shell-known-hostscripts/install_deps.py:482Pipe-to-shell installer from a well-known host (still executes remote code) (string literal in code, not executed)curl -fsSL https://deb.nodesource.com/setup_18.x | bash -
code literal -
low Dangerous commands
cmd-pipe-to-shell-known-hostscripts/install_deps.py:488Pipe-to-shell installer from a well-known host (still executes remote code) (string literal in code, not executed)curl -fsSL https://deb.nodesource.com/setup_18.x | bash -
code literal
Files scanned: 22. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 5174 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 71/100
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 32 mutating operations with no state check
- 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
- 70Inputs and preconditions. Inputs and preconditions are listed
- 70Execution cost. Instruction body is 5174 tokens
- 85Steps. 89 steps, 2 vague phrases
- 100Result and completion. Output format and completion criterion are stated
- 100Failures and branches. 1 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 14 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (4 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
- +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 634: enough signal without eating the budget
- +4Structure: 48 headings
- +3Step-by-step instructions: 89 items
- +3Output format is stated explicitly
- +4Has examples (12 code blocks)
- +4Reference files are cited in the instructions (6 of 6)
- +3All 9 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.