AB huawei-cloud-maas-tokens-usage
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"
As a process B 69/100 · Nearly there — weak spots: result and completion, 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 69/100
- 0Progress reporting. Says nothing while it works
- 40Result and completion. Does not say what the result is
- 55Failures and branches. 1 branches
- 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.