BC geekbi-shopee-product-search-skill
使用极鲸云查询和分析 Shopee 商品、销量与销售额、价格、评分、评论数、点赞、库存、跨境属性、上架时间和历史趋势。用户提到 Shopee 商品搜索、商品 ID、爆品、新品、销量榜、价格带、跨境筛选、增长趋势、候选筛选或单品研究时使用。只依据极鲸云真实返回的数据；不处理图搜、关键词库、评论正文、广告数据、利润或 Seller Centre 操作。
使用极鲸云查询和分析 Shopee 商品、销量与销售额、价格、评分、评论数、点赞、库存、跨境属性、上架时间和历史趋势。用户提到 Shopee 商品搜索、商品 ID、爆品、新品、销量榜、价格带、跨境筛选、增长趋势、候选筛选或单品研究时使用。只依据极鲸云真实返回的数据；不处理图搜、关键词库、评论正文、广告数据、利润或…
As a process C 53/100 · 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.
- 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 · 2
✓ No critical or high findings
Medium and low: 2
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low Secrets in code
secret-high-entropy-tokenreferences/Shopee运营与政策口径.md:33High-entropy token-like string (may be an id, hash or a credential)来源：[Shopee 新加坡商品评分与评论说明](https://help.shopee.sg/portal/4/article/76455-%5BPr…ngs%5D-H…uct)。
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low Secrets in code
secret-high-entropy-tokenskill-card.md:53High-entropy token-like string (may be an id, hash or a credential)- [Shopee product ratings guidance](https://help.shopee.sg/portal/4/article/76455-%5BPr…ngs%5D-H…uct)
Files scanned: 14. 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")
Process rating: all ten parameters 53/100
- 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
- 100Tools and files. No external tools needed
- 100Steps. 16 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 219 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
- +4No input/output examples
- -32 of 5 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 175: enough signal without eating the budget
- +4Structure: 4 headings
- +3Step-by-step instructions: 16 items
- +4Reference files are cited in the instructions (4 of 4)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.