AB huawei-cloud-cloudrobo-infer
Manage CloudRobo inference services — deploy a model into a managed inference service, list/query deployed services and their logs, start/stop a running service, update or delete a service, and orchestrate the 'wait-deploy' convenience flow that polls a service until it finishes deploying. Inference services consume models produced by cloudrobo-train and are consumed by robo-dispatcher when dispatching embodied tasks. Triggers include: infer, inference, model deployment, deploy model, inference service, service deployment, start inference, stop inference, service logs, wait-deploy, model serving, 推理, 推理服务, 模型部署, 部署模型, 推理服务管理, 服务日志, 模型服务.
Manage CloudRobo inference services — deploy a model into a managed inference service, list/query deployed services and their logs, start/stop a running…
As a process B 71/100 · Nearly there — weak spots: result and completion
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 · 0
✓ No critical or high findings
Files scanned: 11. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 6972 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 71/100
- 40Result and completion. Does not say what the result is
- 50When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
- 70Inputs and preconditions. Inputs and preconditions are listed
- 70Execution cost. Instruction body is 6972 tokens
- 85Steps. 102 steps, 2 vague phrases
- 100Failures and branches. 7 branches, has a failure section
- 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
- low The response is described with custom markup (26 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)
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +3Description length 645: enough signal without eating the budget
- +4Structure: 21 headings
- +3Step-by-step instructions: 102 items
- +4Has examples (7 code blocks)
- +4Reference files are cited in the instructions (6 of 7)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.