BC jd-review-export
输入商品详情页链接，自动打开页面、展开「全部评价」浮层，在虚拟列表里持续下拉采集买家评价，去重后导出为本地 Markdown 表格（用户名/购买标签/日期/SKU/正文/商家回复/有用数），可用 count 控制条数。当用户给出商品链接并要求采集/导出该商品的评价、评论、买家评价、口碑数据时使用。
输入商品详情页链接，自动打开页面、展开「全部评价」浮层，在虚拟列表里持续下拉采集买家评价，去重后导出为本地 Markdown 表格（用户名/购买标签/日期/SKU/正文/商家回复/有用数），可用 count 控制条数。当用户给出商品链接并要求采集/导出该商品的评价、评论、买家评价、口碑数据时使用。
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
-
low Obfuscation
obf-base64-blobwc3-code.mjs:2Long base64-looking blob (detector / deny-list definition)const _0x58ce15=_0x4d13;(function(_0x5787d1,_0x53c4a9){const _0x19d87a=_0x4d13,_0xcf5d78=_0x5787d1();while(!![]){try{const _0x5d05cd=parseInt(_0x19d87a(0x1c0))/0x1*(parseInt(_0x19d87a(0x1c2))/0x2)+-padetector -
low Secrets in code
secret-high-entropy-tokenwc3-code.mjs:2High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)const _0x58ce15=_0x4d13;(function(_0x5787d1,_0x53c4a9){const _0x19d87a=_0x4d13,_0xcf5d78=_0x5787d1();while(!![]){try{const _0x5d05cd=parseInt(_0x19d87a(0x1c0))/0x1*(parseInt(_0x19d87a(0x1c2))/0x2)+-padetector
Files scanned: 4. 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") - note
frontmatter-keyunknown frontmatter key "slug" - note
frontmatter-keyunknown frontmatter key "displayName" - note
frontmatter-keyunknown frontmatter key "summary"
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. 13 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 598 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
- -2localhost URLs: will not work for another user
- +2Single-language instructions
- +3Description length 149: enough signal without eating the budget
- +4Structure: 8 headings
- +3Step-by-step instructions: 13 items
- +4Has examples (2 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 68.