AC huawei-cloud-flexus-l-server-ops
Based on Huawei Cloud Flexus L API for instance management and operations. Supports querying instance list and details, querying traffic packages, batch start/stop/reboot instances, resetting passwords, and modifying instance information. Suitable for daily operations, lifecycle management, configuration changes, traffic monitoring, and other scenarios for Flexus L instances. Triggers: Flexus L, Huawei Cloud ops, query instance, start, stop, reboot, reset password, modify info, check traffic, traffic package (中文触发词: L实例运维，查询L实例，L实例开机，L实例关机，L实例重启，L实例重置密码，查询L实例流量包).
Based on Huawei Cloud Flexus L API for instance management and operations.
As a process C 60/100 · Has gaps — weak spots: result and completion, running it twice
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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: 10. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 60/100
- 0Result and completion. Does not say what the result is
- 30Running it twice. 1 mutating operations with no state check
- 50When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
- 70Inputs and preconditions. Inputs and preconditions are listed
- 70Failures and branches. 4 branches
- 70Execution cost. Instruction body is 4588 tokens
- 85Steps. 80 steps, 1 vague phrases
- 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
- low 10 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
- +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
- -230 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +3Description length 573: enough signal without eating the budget
- +4Structure: 35 headings
- +3Step-by-step instructions: 80 items
- +4Has examples (20 code blocks)
- +4Reference files are cited in the instructions (2 of 2)
- +3All 6 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.