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BC cloud-infra-automation

Automate provisioning and management of AWS, GCP, and Azure infrastructure using Terraform, Ansib... 核心能力: - 效率工具领域的专业化AI辅助工具 - 基于高人气开源Skill深度优化升级 - 移除风险代码,增强安全性和稳定性 适用场景: - 工作流自动化、任务调度、批处理 - 独立开发者与一人公司效率提升 - 自动化工作流与智能决策辅助 差异化:经过深度优化,去除原始风险代码,清理外部依赖引用,增强元数据和触发关键词,完全适配SkillHub平台规范。 触发关键词: cloud, azure, automation, infra, infrastructure, management, provisioning, automate

ClawHub Agent Skills author: 天轰穿 v1.0.0 MIT-0 2 files body ≈ 275 tokens Open the sourceclawhub.ai↗ analyzed 21 h ago

Automate provisioning and management of AWS, GCP, and Azure infrastructure using Terraform, Ansib... 核心能力: - 效率工具领域的专业化AI辅助工具 - 基于高人气开源Skill深度优化升级 -…

As a process C 58/100 · Has gaps — weak spots: result and completion, when it triggers, failures and branches

IntegrationAWSTerraformGoogle CloudAzureInfrastructuretype 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
B
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
70
Run on models
none yet
Process rating
C
58/100
Has gaps
Result and completion w 14
0
Failures and branches w 10
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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: 2. 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")
  • note frontmatter-key unknown frontmatter key "slug"
  • note frontmatter-key unknown frontmatter key "displayName"
  • note frontmatter-key unknown frontmatter key "summary"
  • note frontmatter-key unknown frontmatter key "tools"

Process rating: all ten parameters 58/100

  • 0Result and completion. Does not say what the result is
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 1 mutating operations with no state check
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Tools and files. Tools declared in frontmatter
  • 100Steps. 21 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 275 tokens

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)
  • +3Output format is not stated: the model decides each time
  • +2Single-language instructions
  • +3Description length 382: enough signal without eating the budget
  • +4Structure: 14 headings
  • +3Step-by-step instructions: 21 items
  • +4Has examples (1 code blocks)
  • +1License stated

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

External checks

ClawHub: suspicious
This skill is for cloud infrastructure automation, but it includes broad auto-trigger terms and an unguarded production destroy example that users should review carefully before installing.
LLM: suspicious (high) · 17 Jul 2026