watcha-finder
Find, evaluate, and recommend AI products using the watcha.cn platform API. Use this skill whenever the user asks about AI tools, AI products, AI apps, or wants to discover/compare/evaluate AI products in China or globally. Also use when the user mentions watcha, watcha.cn, or wants product recommendations for specific use cases (e.g., "what's a good AI coding tool?", "find me an AI video generator", "哪个AI写作工具好用"). This skill knows how to search, filter, read reviews, and cross-reference with web sources to give well-rounded product assessments — not just popularity rankings.
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/charipoter/watcha-finderWatcha AI Product Finder
You have access to the watcha.cn API — a Chinese AI product discovery platform with 1000+ products, user reviews, and community discussions. Your job is to help the user find AI products that genuinely fit their needs, not just the most popular ones.
Core Principle: Popularity ≠ Quality
The watcha.cn community has biases you need to account for:
- Review count and reply count reflect how talked about a product is (热度/hype), not how good it is. A niche but excellent tool may have 2 reviews; a mediocre but well-marketed tool may have 50.
- Scores (
stats.score) are only meaningful whenreview_countis substantial (roughly 10+). A score of 9.0 from 2 reviews tells you almost nothing. A score of 7.5 from 40 reviews is much more informative. score_revealedbeingfalsemeans the score isn't shown publicly yet (too few reviews). Treat these products as "unproven" rather than "bad."- Upvotes vs downvotes can hint at community sentiment but are gameable.
Because of these limitations, always supplement watcha data with web searches to get a fuller picture — especially for products with few reviews.
API Reference
All requests go to https://watcha.cn/api/v2/. Use these headers:
accept: application/json, text/plain, */*
content-type: application/json; charset=UTF-8
origin: https://watcha.cn
referer: https://watcha.cn/products
user-agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/144.0.0.0 Safari/537.36
1. Search Products
POST /search/general?q={query}&skip={offset}&limit={count}
Body: {"options":{"domains":["product"],"product_options":{"facets":["category_ids","tag_ids"]}}}
Filtering — add to product_options:
"category_ids": [6]— filter by category"tag_ids": [4]— filter by tag
Search is exact-match, not fuzzy. If the user says "video editing AI", try multiple queries:
- The exact product name if known
- English keywords:
"video editor","video","editing" - Chinese keywords:
"视频编辑","视频创作","视频" - Or skip the query entirely (
q=) and filter by category instead
When a text query returns few/no results, fall back to category browsing with q= (empty) and the relevant category_ids.
Categories:
| ID | Name | English |
|---|---|---|
| 1 | 通用助手 | General Assistant |
| 2 | 写作辅助 | Writing |
| 3 | 图像生成 | Image Generation |
| 4 | 视频创作 | Video Creation |
| 5 | 音频处理 | Audio Processing |
| 6 | 编程开发 | Coding/Dev |
| 7 | 智能搜索 | Smart Search |
| 8 | 知识管理 | Knowledge Management |
| 9 | 科研辅助 | Research |
| 10 | 智能硬件 | Smart Hardware |
| 11 | 虚拟陪伴 | Virtual Companion |
| 12 | 其他类型 | Other |
| 13 | Agent 构建 | Agent Building |
| 14 | 效率工具 | Productivity |
| 15 | 3D 生成 | 3D Generation |
Tags (for tag_ids):
Metadata
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Find the right skillPaste this into your clawhub.json to enable this plugin.
{
"plugins": {
"official-charipoter-watcha-finder": {
"enabled": true,
"auto_update": true
}
}
}