BF linkfox-kalodata-tiktok-product
通过kalodata数据查询TikTok电商商品排行榜并查询指定商品的详细数据，支持按地区、货币、语言与日期范围查看高排名/热销商品，并可用productId获取价格区间、销量、销售额、佣金率、上下架时间及所属店铺。当用户提到TikTok商品榜单、TikTok商品排行、TikTok热销榜、TikTok爆品排行、TikTok选品榜单、kalodata商品榜、TikTok商品详情、TikTok商品资料、商品价格、商品销量、TikTok product ranking, TikTok bestseller chart, TikTok top products, kalodata product rank, TikTok product detail, kalodata product detail, product analytics时触发此技能。即使用户未明确提及"kalodata"，只要其需求涉及查看TikTok平台的商品排行榜或某个TikTok商品的详细数据，也应触发此技能。
通过kalodata数据查询TikTok电商商品排行榜并查询指定商品的详细数据，支持按地区、货币、语言与日期范围查看高排名/热销商品，并可用productId获取价格区间、销量、销售额、佣金率、上下架时间及所属店铺。当用户提到TikTok商品榜单、TikTok商品排行、TikTok热销榜、TikTok爆品排行、Tik…
As a process F 46/100 · Will not run — References files that are not bundled: references/api.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/api.md
Process rating: all ten parameters 46/100
- 0Tools and files. 1 referenced file(s) missing: references/api.md
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 50Failures and branches. 0 branches, has a failure section
- 70When it triggers. States when to use, but not when not to
- 100Steps. 35 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2239 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 (8 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
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +3Description length 444: enough signal without eating the budget
- +4Structure: 12 headings
- +3Step-by-step instructions: 35 items
- +4Has examples (3 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 66.