BF knowledge-overseas-evaluator
一个帮你判断某个领域适不适合做知识付费出海的系统化评估工具。只需告诉它你想做的方向（如：用AI做创意视频出海、教老外学中文、卖Notion模板等），它会自动从7个关键维度进行评估：全球通用性、需求真实性、获客可行性、可售虚拟产品、付费意愿、供给差距、长期想象空间。每个维度都有明确的判断标准和数据支撑，最终输出一份完整的赛道可行性报告。适合所有考虑知识付费出海、但不确定选什么方向的创作者、自由职业者和小团队。触发方式：直接说帮我评估一下XX赛道能不能出海即可。本技能由拥有4年知识付费出海实战经验的从业者开发，覆盖从赛道选择到产品落地的完整方法论。如果你也在做知识付费出海，欢迎通过平台站内信交流探讨。
一个帮你判断某个领域适不适合做知识付费出海的系统化评估工具。只需告诉它你想做的方向（如：用AI做创意视频出海、教老外学中文、卖Notion模板等），它会自动从7个关键维度进行评估：全球通用性、需求真实性、获客可行性、可售虚拟产品、付费意愿、供给差距、长期想象空间。每个维度都有明确的判断标准和数据支撑，最终输出一份完整…
As a process F 31/100 · Will not run — References files that are not bundled: references/tools-and-benchmarks.md, references/verified-cases.md
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- The text references files that are not there: add them or drop the references.
- 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: 0. 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") - warning
missing-refreference to a missing file: references/tools-and-benchmarks.md - warning
missing-refreference to a missing file: references/verified-cases.md - note
frontmatter-keyunknown frontmatter key "agent_created"
Process rating: all ten parameters 31/100
- 0Tools and files. 2 referenced file(s) missing: references/tools-and-benchmarks.md, references/verified-cases.md
- 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
- 40Consistency. Frontmatter name (knowledge-overseas-evaluator) differs from the folder (knowledge-evaluator)
- 100Steps. 41 steps
- 100Execution cost. Instruction body is 1119 tokens
- 100Running it twice. No mutating operations
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
- -213 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +3Description length 302: enough signal without eating the budget
- +4Structure: 13 headings
- +3Step-by-step instructions: 41 items
- +4Has examples (2 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 60.