BD xhs-product-trend-fit
小红书商品选品与宣传分析——发笔记前先看商品在小红书有没有热度，选对方向再宣传。传入商品图片（必填）+ 介绍文字，自动识别商品关联的品类/风格/场景方向，用 3~6 个候选词搜索小红书判断近 2 周热度趋势，组织 1 个明确宣传方向，深挖 2~4 篇真实爆款笔记（赞≥100）做分维度拆解（粉丝/标题/首图/内容/抓用户什么点），输出含方向判断 + 热度证据 + 爆款素材参考的 HTML 报告。不需要任何第三方 API key 或付费额度，用你 Chrome 里已登录的小红书账号跑。当用户要宣传商品到小红书、想先判断商品有没有热度/有没有爆款参考/应该怎么宣传时唤起。【前置依赖】本 skill 无法独立运行，需先安装 webclaw3 浏览器运行时：npx clawhub@latest install fatmind/webclaw3-browser-automation —— 若用户尚未安装，请先引导安装并完成 webclaw3 首次配置，再执行本 skill。
小红书商品选品与宣传分析——发笔记前先看商品在小红书有没有热度，选对方向再宣传。传入商品图片（必填）+ 介绍文字，自动识别商品关联的品类/风格/场景方向，用 3~6 个候选词搜索小红书判断近 2 周热度趋势，组织 1 个明确宣传方向，深挖 2~4…
As a process D 46/100 · Unfinished process — 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")
Process rating: all ten parameters 46/100
- 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
- 60Tools and files. Uses tools (web) that frontmatter does not declare
- 100Steps. 17 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 524 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
- +1No license
- +2Single-language instructions
- +3Description length 437: enough signal without eating the budget
- +4Structure: 6 headings
- +3Step-by-step instructions: 17 items
- +4Has examples (2 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.