BC token-economist-pro
Token经济学家（专业版）在免费版基础上解锁多级向量语义缓存、Token成本预估与月度预算控制、团队成本剖析、基于LLM的智能摘要与上下文图压缩、缓存命中率调优、多模型智能路由等高级能力. 适用于需要token economist相关能力的开发场景,提供结构化的工作流程和配置指引. 该工具经过深度差异化处理,针对用户反馈和使用痛点进行了优化改进,提升了实用性和可操作性.
Token经济学家（专业版）在免费版基础上解锁多级向量语义缓存、Token成本预估与月度预算控制、团队成本剖析、基于LLM的智能摘要与上下文图压缩、缓存命中率调优、多模型智能路由等高级能力.
As a process C 52/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
- 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: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-long-hermesdescription is 187 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period) - warning
description-no-whenneither description nor a "## When to Use" section says when to use the skill - warning
body-longSKILL.md body ≈ 5478 tokens (recommended < 5000); move details to references/ - note
frontmatter-keyunknown frontmatter key "slug" - note
frontmatter-keyunknown frontmatter key "displayName" - note
frontmatter-keyunknown frontmatter key "summary" - note
frontmatter-keyunknown frontmatter key "edition" - note
frontmatter-keyunknown frontmatter key "tools" - note
frontmatter-keyunknown frontmatter key "homepage" - note
frontmatter-keyunknown frontmatter key "pricing_tier"
Process rating: all ten parameters 52/100
- 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
- 20When it triggers. No condition that starts the skill
- 70Execution cost. Instruction body is 5478 tokens
- 100Tools and files. Tools declared in frontmatter
- 100Steps. 69 steps
- 100Consistency. Name and required fields are in place
- 100Running it twice. No mutating operations
- low 20 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
- +2Single-language instructions
- +3Description length 187: enough signal without eating the budget
- +4Structure: 62 headings
- +3Step-by-step instructions: 69 items
- +4Has examples (26 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 54.