CB huawei-cloud-iac-reverse
Reverse-engineer existing Huawei Cloud resources into deployable Terraform IaC code. Queries all resources via RMS (Config/配置审计) API, analyzes specs and topology service-by-service, then synthesizes Terraform HCL configurations with provider validation. Use when the user wants to generate IaC from existing cloud resources, migrate infrastructure to code, or reverse-engineer cloud topology. Triggers include: "逆向生成IaC", "反向生成Terraform", "从现有资源生成代码", "reverse engineer IaC", "generate Terraform from existing resources", "infrastructure to code", "cloud to IaC".
Reverse-engineer existing Huawei Cloud resources into deployable Terraform IaC code.
As a process B 65/100 · Nearly there — weak spots: result and completion, failures and branches, running it twice
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.
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.
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
- 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.
- 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 · 1
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high Dangerous commands
cmd-pipe-to-shellreferences/cli-installation-guide.md:15Downloads and executes remote code from an unrecognised host (pipe to shell)curl -sSL https://cn-n…com/cli/install.sh | bash
Files scanned: 11. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
frontmatter-keyunknown frontmatter key "triggers" - note
edit-residuethe text marks something as outdated (lines 98, 102): check that old rules are not kept next to new ones — the full check reads the text for contradictions
Process rating: all ten parameters 65/100
- 0Failures and branches. Linear process with no failure handling
- 30Running it twice. 3 mutating operations with no state check
- 40Result and completion. Does not say what the result is
- 60Tools and files. Uses tools (bash, node) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 49 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2422 tokens
- 100Progress reporting. Reports progress
- low 11 top-level sections: this looks like several domains in one skill
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
- +1No license
- +2Single-language instructions
- +5Description quotes 7 example trigger phrases
- +3Description length 563: enough signal without eating the budget
- +4Structure: 23 headings
- +3Step-by-step instructions: 49 items
- +4Has examples (6 code blocks)
- +4Reference files are cited in the instructions (5 of 5)
- +3All 3 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 95.