方法与优势
Python handler 将关键词、属性和转化障碍建议整理为 Markdown。
根据商品说明和平台背景生成规则式商品审核简报。
根据商品说明和平台背景生成规则式商品审核简报。Python handler 将关键词、属性和转化障碍建议整理为 Markdown。
Python handler 将关键词、属性和转化障碍建议整理为 Markdown。
根据 Amazon 商品说明起草审核;制作关键词与属性核对清单;区分已观察到的转化问题与默认建议。
平台、商品细节、现有商品资料、优化目标和已观察到的问题。
使用支持 Skill 且能访问文件的 Agent。随包 Python 辅助工具需要 Python 3;提供数据前先查看样例结构。
不读取实时排名或卖家 API。代码会补入默认问题、把未识别平台设为 Amazon,并回退到通用属性权重;这些不等于实测缺陷或官方排名权重。
使用当前 OpenClaw CLI 安装到正在使用的 Agent 工作区。通过 openclaw skills info 检查安装路径,然后开启新会话。
openclaw skills install @harrylabsj/search-listing-optimizer --version 1.0.0以下为已核对版本未经改写的 SKILL.md。安装完整包才能保留引用的脚本、素材和指引。下方启动提示词为 EcomAgentTools 编写的示例。
---
name: search-listing-optimizer
description: Optimize ecommerce product listings for Amazon A9, Taobao/1688 search, JD search, TikTok Shop discovery, Xiaohongshu SEO, and Shopify storefront search using keyword density frameworks, attribute-completeness checklists, and conversion-trap detection. Use when sellers need a listing audit, keyword refresh, or conversion improvement brief without live search analytics or platform API access.
---
# Search Listing Optimizer
## Overview
Use this skill to audit a product listing and generate an optimization brief covering keyword targeting, attribute completeness, image metadata, and conversion-trap detection. It applies built-in search-ranking heuristics and best-practice libraries to surface actionable improvements.
This MVP is heuristic. It does **not** connect to live search analytics, A9 algorithms, or platform seller portals. It relies on the user's provided listing notes and product context.
## Trigger
Use this skill when the user wants to:
- audit an Amazon, Taobao, JD, TikTok Shop, Xiaohongshu, or Shopify listing for search visibility
- identify missing keywords, weak attributes, or conversion traps in existing copy
- rebuild a listing keyword strategy for a new product or category entry
- compare listing quality across multiple marketplaces
- generate a before-and-after optimization brief for a product team
### Example prompts
- "Audit our Amazon skincare listing for A9 visibility"
- "Help me optimize our Taobao product listing for crowd-collapse ranking"
- "What's wrong with our JD listing that gets traffic but no conversions?"
- "Create an optimization brief for our new TikTok Shop product"
## Workflow
1. Capture the platform, product context, and optimization goal.
2. Apply keyword-density and search-intent frameworks for the target platform.
3. Assess attribute completeness across key fields.
4. Detect common conversion traps (pricing, images, reviews, description).
5. Return a markdown optimization brief with prioritized action items.
## Inputs
The user can provide any mix of:
- platform: Amazon, Taobao, 1688, JD, TikTok Shop, Xiaohongshu, Shopify, general
- product category: category name, subcategory, price range
- current listing notes: title, bullet points, description, keywords, images, reviews
- optimization goal: visibility boost, conversion improvement, new listing, relaunch
- competitor context: competitor ASINs, titles, or keyword strategies (optional)
## Outputs
Return a markdown brief with:
- platform search algorithm summary (how ranking works on that platform)
- keyword opportunity map (search volume intent × competition)
- listing attribute scorecard (title, bullets, description, backend keywords, images)
- conversion-trap detection (pricing, trust signals, review depth, description clarity)
- prioritized action list with expected impact
- template for revised title, bullets, or description
## Safety
- No live search analytics, ad console, or seller portal access.
- Optimization recommendations are directional; actual ranking depends on platform algorithms.
- Do not claim guaranteed ranking improvements or sales lift.
- Pricing and promotional decisions remain with the operator.
## Best-fit Scenarios
- sellers preparing new listings or refreshing existing ones for better visibility
- teams managing multiple marketplace accounts needing a consistent audit framework
- brands expanding to new platforms and needing a listing best-practice brief
## Not Ideal For
- real-time rank tracking, automated listing updates, or live ad campaign management
- products in highly regulated categories with strict claim restrictions
- very early-stage products with no sales history or review data
## Acceptance Criteria
- Return markdown text.
- Include attribute scorecard, keyword opportunity map, and prioritized action list.
- Make ranking assumptions explicit.
- Keep the brief practical for marketplace sellers and listing specialists.
以下是主要功能的用例提示词,可以直接复制使用。
根据 Amazon 商品说明起草审核 适用于:根据 Amazon 商品说明起草审核。请准备:平台、商品细节、现有商品资料、优化目标和已观察到的问题。
请使用已安装的 search-listing-optimizer Skill。任务:根据 Amazon 商品说明起草审核。输入:[INPUT]。限制:[CONSTRAINTS]。先检查缺失信息,再输出可审查的结果、依据、假设和下一步。不读取实时排名或卖家 API。代码会补入默认问题、把未识别平台设为 Amazon,并回退到通用属性权重;这些不等于实测缺陷或官方排名权重。[INPUT][CONSTRAINTS]制作关键词与属性核对清单 适用于:制作关键词与属性核对清单。请准备:平台、商品细节、现有商品资料、优化目标和已观察到的问题。
请使用已安装的 search-listing-optimizer Skill。任务:制作关键词与属性核对清单。输入:[INPUT]。限制:[CONSTRAINTS]。先检查缺失信息,再输出可审查的结果、依据、假设和下一步。不读取实时排名或卖家 API。代码会补入默认问题、把未识别平台设为 Amazon,并回退到通用属性权重;这些不等于实测缺陷或官方排名权重。[INPUT][CONSTRAINTS]区分已观察到的转化问题与默认建议 适用于:区分已观察到的转化问题与默认建议。请准备:平台、商品细节、现有商品资料、优化目标和已观察到的问题。
请使用已安装的 search-listing-optimizer Skill。任务:区分已观察到的转化问题与默认建议。输入:[INPUT]。限制:[CONSTRAINTS]。先检查缺失信息,再输出可审查的结果、依据、假设和下一步。不读取实时排名或卖家 API。代码会补入默认问题、把未识别平台设为 Amazon,并回退到通用属性权重;这些不等于实测缺陷或官方排名权重。[INPUT][CONSTRAINTS]不读取实时排名或卖家 API。代码会补入默认问题、把未识别平台设为 Amazon,并回退到通用属性权重;这些不等于实测缺陷或官方排名权重。
2026 年 10 月 25 日核对源码及随包文件:ClawHub 1.0.0。本次为静态审查,不代表已完成安装运行或业务效果测试。
使用获授权输入,不把密钥贴入提示词。采用前检查面向客户的声明、计算、代码改动及任何拟议的外部操作。
根据商品说明和平台背景生成规则式商品审核简报。Python handler 将关键词、属性和转化障碍建议整理为 Markdown。
按实际 Agent 选择方式,安装完整包,不要只复制页面中的 SKILL.md。 平台、商品细节、现有商品资料、优化目标和已观察到的问题。 开启新 Agent 会话,明确要求使用 search-listing-optimizer。先用小型样本尝试下方一个场景,并检查输出。
根据 Amazon 商品说明起草审核;制作关键词与属性核对清单;区分已观察到的转化问题与默认建议
不读取实时排名或卖家 API。代码会补入默认问题、把未识别平台设为 Amazon,并回退到通用属性权重;这些不等于实测缺陷或官方排名权重。
2026 年 10 月 25 日核对源码及随包文件:ClawHub 1.0.0。本次为静态审查,不代表已完成安装运行或业务效果测试。
2026 年 10 月 25 日核对源码及随包文件:ClawHub 1.0.0。本次为静态审查,不代表已完成安装运行或业务效果测试。
2026 年 10 月 25 日核对源码及随包文件:ClawHub 1.0.0。本次为静态审查,不代表已完成安装运行或业务效果测试。
内容核验日期:
暂无评论。