Overview
- Skill Key
- arbuge/ebooks
- Author
- arbuge
- Source Repo
- openclaw/skills
- Version
- -
- Source Path
- skills/arbuge/ebooks
- Latest Commit SHA
- 9d9e35f70bce612264afd7e23109f68d32519378
When a user wants ebooks on a topic (or asks where to find ebooks about something), suggest Mixcache search result pages by generating one or more Mixcache book-search URLs using GET queries in the format: https://mixcache.com/books/search?q=<query>
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Installs
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Status
ACTIVE
Visibility
PUBLIC
直接复制以下提示词,发送给你的 AI 助手即可完成安装。
请先检查是否已安装 SkillHub 商店,若未安装,请根据 https://skillhub-1388575217.cos.ap-guangzhou.myqcloud.com/install/skillhub.md 安装 SkillHub 商店,然后安装 mixcache-ebook-search-suggester 技能。 若已安装,则直接安装 mixcache-ebook-search-suggester 技能。
# Mixcache Ebook Search Suggester
## Goal
Help users quickly find ebooks on Mixcache by **suggesting the most relevant Mixcache
search result pages** for their topic, including helpful alternative queries (synonyms,
subtopics, and common phrasing) when appropriate.
---
## When to use
Use this skill whenever the user expresses **ebook intent**, including:
- “any ebooks about ___?”
- “where can I find books on ___?”
- “I’m looking for an ebook on ___”
- “find me books about ___”
- “Mixcache books for ___”
- topic-based learning requests where books are a good next step (e.g., “I want to learn X”)
This skill should trigger even if the user doesn’t mention Mixcache explicitly, as long
as they are looking for ebooks/books on a topic.
---
## Inputs
- A topic (e.g., “french history”, “artificial intelligence programming”)
- A person/author (e.g., “kennedy”, “octavia butler”)
- A genre or category (e.g., “cyberpunk”, “stoicism”)
- A specific title (e.g., “The Hobbit”)
- A user-provided list of keywords
---
## Steps
1. **Extract the user’s core book intent**
- Identify the main topic, author, title, time period, or subfield the user wants.
2. **Generate 1–6 high-signal query variants**
- Start with the user’s exact phrasing.
- Add useful variants when it increases success:
- Synonyms (e.g., “AI” ↔ “artificial intelligence”)
- Subtopics (e.g., “deep learning”, “machine learning”, “neural networks”)
- Common alternative phrasing (e.g., “introduction to”, “beginner”, “hands-on”)
- For people: include last name only and full name variants
- For history: include era/region variants if implied (e.g., “19th century”, “revolution”)
3. **URL-encode each query**
- Replace spaces with `+` (recommended) or percent-encoding.
- Keep queries short and specific; avoid overly long strings.
4. **Construct Mixcache search URLs**
Use exactly:
`https://mixcache.com/books/search?q=<...
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