BC workbuddy-usage-status
离线可视化 WorkBuddy 本机使用数据，以 token 消耗为主指标、credit 为逐次实测精确值，涵盖思考效率、模型分布、成本与费率、单次提问成本、缓存命中率、日期区间筛选、错误监控、用量高峰探查，生成本地使用信息看板，并同步导出全量 CSV 与 xlsx。仅当用户**明确**想查看、生成或导出**自己 WorkBuddy 本机/本账号**的使用状态 / 使用统计 / 工作信息看板时调用；不用于其他产品或系统的用量统计，也不为任意数据生成通用看板。纯本地、全程零网络、可搬运；可选 --credit-xlsx 作参考补充，只在本地缺少逐次明细的日期上补入。 EN: Offline dashboard for WorkBuddy local usage analytics, with token as primary metric and credit measured per call, covering thinking efficiency, model distribution, model cost & rates, costliest single prompts, cache hit rate, date-range filtering, error monitoring, usage-spike inspection; a full CSV and xlsx export is written on every run. Triggers only when the user explicitly wants to view, generate, or export their own WorkBuddy local/account usage status / stats / activity dashboard; not for other products' usage analytics, nor for building generic dashboards from arbitrary data. Fully local and zero-network; the optional --credit-xlsx serves as a reference supplement only, filling days that lack local per-call detail.
离线可视化 WorkBuddy 本机使用数据，以 token 消耗为主指标、credit 为逐次实测精确值，涵盖思考效率、模型分布、成本与费率、单次提问成本、缓存命中率、日期区间筛选、错误监控、用量高峰探查，生成本地使用信息看板，并同步导出全量 CSV 与 xlsx。仅当用户明确想查看、生成或导出自己…
As a process C 63/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches
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: 9. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
frontmatter-keyunknown frontmatter key "slug" - note
frontmatter-keyunknown frontmatter key "displayName" - note
frontmatter-keyunknown frontmatter key "agent_created" - note
frontmatter-keyunknown frontmatter key "summary" - note
frontmatter-keyunknown frontmatter key "trigger"
Process rating: all ten parameters 63/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 100Tools and files. Tools declared in frontmatter
- 100Steps. 40 steps
- 100When it triggers. States when to use and when not to
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1328 tokens
- 100Running it twice. No mutating operations
- low 10 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (4 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
- +3Description length 1022: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +2Single-language instructions
- +4Description says when NOT to use the skill
- +4Structure: 10 headings
- +3Step-by-step instructions: 40 items
- +4Has examples (1 code blocks)
- +3All 2 scripts are documented
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.