AC huawei-cloud-gitsync-nativetest
Query Huawei Cloud MaaS (Model as a Service) tokens usage statistics, including total tokens, prompt tokens, completion tokens, total requests, and total errors. Supports preset service, my service, and custom endpoint with time range queries (last 7/14/30 days or custom). Data source is MaaS ShowStatistics API, consistent with console. Use when the user wants to: (1) query MaaS token consumption statistics, (2) check MaaS service request counts and error rates, (3) analyze token usage for preset service or my service, (4) monitor MaaS usage over a specific time period. Triggers include: "MaaS", "Model as a Service", "tokens usage", "token consumption", "request count", "error count", "MaaS usage", "preset service usage", "completion tokens", "prompt tokens", "MaaS statistics", "模型服务", "令牌用量", "token统计", "token用量", "词元用量", "请求次数", "MaaS监控", "华为云MaaS"
Query Huawei Cloud MaaS (Model as a Service) tokens usage statistics, including total tokens, prompt tokens, completion tokens, total requests, and total…
As a process C 63/100 · Has gaps — weak spots: result and completion, failures and branches, progress reporting
How to improve
- 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: 13. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 63/100
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 40Result and completion. Does not say what the result is
- 60Tools and files. Uses tools (python) 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
- 85Steps. 6 steps, 1 vague phrases
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1784 tokens
- 100Running it twice. No mutating operations
- 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
- +4Description does not say when NOT to use the skill (false activations)
- +3Description length 862: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +5Description quotes 14 example trigger phrases
- +4Structure: 12 headings
- +3Step-by-step instructions: 6 items
- +4Has examples (6 code blocks)
- +4Reference files are cited in the instructions (8 of 8)
- +3All 2 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 93.