Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add alchaincyf/huashu-weread --skill huashu-wereadgit clone --depth 1 https://github.com/alchaincyf/huashu-wereadWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/alchaincyf/huashu-weread/huashu-weread)<a href="https://agentmods.dev/skills/alchaincyf/huashu-weread/huashu-weread"><img src="https://agentmods.dev/badge/skills/alchaincyf/huashu-weread/huashu-weread/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/alchaincyf/huashu-weread/huashu-weread"><img src="https://agentmods.dev/badge/skills/alchaincyf/huashu-weread/huashu-weread.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00240 | $0.02974 |
| Opus 5 | $0.00120 | $0.01487 |
| Sonnet 5 | $0.00048 | $0.00595 |
| Haiku 4.5 | $0.00024 | $0.00297 |
Grade A, and why
huashu-weread-advisor scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
huashu-weread-advisor
把原子的微信读书 API 变成一个真正读懂你的读书顾问。
定位
底层 weread skill 提供原子接口（搜索、书架、笔记、点评、推荐、阅读统计），本 skill 在其之上做工作流编排，把原始数据转成对用户有消费价值的产出。
前置依赖
- 必须先有
WEREAD_API_KEY环境变量（在用户 shell 中 export） - 所有 API 调用走
POST https://i.weread.qq.com/api/agent/gateway - 请求 body 必须带
skill_version字段——值的权威来源：~/.claude/skills/weread/SKILL.md顶部 frontmatter 的version字段（当前1.0.3，会变；别从 prompt 或老模板里抄） - 接口文档和参数详情见底层 weread skill：
~/.claude/skills/weread/SKILL.md
核心方法论（所有 workflow 共享）
1. 书架和笔记是两个数据源，必须交叉
| 数据源 | 接口 | 揭示什么 |
|---|---|---|
| 书架 | /shelf/sync |
用户主动分类的兴趣方向 + 加入了什么 |
| 笔记 | /user/notebooks |
用户真读过的书 + 读得多深（笔记条数） |
| 进度 | /book/getprogress |
某本书读到哪、累计读了多久 |
| 统计 | /readdata/detail |
周/月/年阅读时长、天数、主题偏好 |
关键洞察：很多书在书架但没动，很多书没在书架（借/试读）但深读了。只看书架会漏掉重要信号。
实战例子：花叔的 Kandel《追寻记忆的痕迹》27 条笔记，书架的「心理学」分类里根本没列，但其实是他在神经科学领域读得最深的一本。如果只看书架做推荐，会误判他的真实知识地图。
2. 「最近读什么」≠「书架主题」
用户的当前兴趣可能和书架分类完全不一致。永远用 readUpdateTime 倒序看最近 30 天在动什么书，再做推荐。
3. 推荐必附 weread:// 深度链接
weread://reading?bId={bookId} 让用户一键打开。链接格式详见底层 weread skill 的「深度链接（URL Schema）」章节。
4. 推荐前必须验证微信读书是否上架
用 /store/search 搜确认。上架的附 weread:// 链接，不上架的明确告诉用户合法替代路径（购买纸质/英文版/作者公开课/图书馆）。绝不推盗版资源。
5. 输出走花叔语言风格
- 不堆砌、不破折号（全文 ≤ 2 处）、人味重
- 用「」不用""
- 不用「首先/其次/综上」这类 AI 结构词
- 不用「说白了/简单来说/换句话说」
- markdown 不过度加粗
- 详见
/04-写作参考/SHARED-RULES.md
检查点设计原则
所有 workflow 必须在「分叉影响输出本质」的地方插入用户确认 gate，防止 AI 默认值跑偏：
- 推荐数量分叉：advisor 推 3 本 vs 8 本完全不同的体验，不要默认 5 本，先问
- 平台语气分叉：复盘文章发朋友圈/公众号/小红书/视频脚本语气差很多，写前必须确认
- 段位判断分叉：path workflow 把「我以为你是入门」的判断给用户看，让他确认或纠正
- 数据量分叉：alchemy 跨主题模式拉出 50+ 划线时，先汇总议题让用户选子集，不要默认全聚
- 未上架处理分叉：推荐里要不要包含未上架的书（用户可能只想要点开就能读的）
检查点不是「每步都问」。日常小决策（哪本放第一梯队、用什么动词）AI 自己定，不要打扰用户。规则是：只在选项影响输出本质时问。
如果用户原始 prompt 已经明确指定（「推 3 本上架的发公众号」），所有相关检查点都跳过。
子命令路由
| 用户说什么 | 走哪个 workflow |
|---|---|
| 推荐书 / 下一本读啥 / 不知道读啥 / 想读 X 方向 | advisor.md |
| 想搞懂 X 这个领域 / 系统学习 X / 从零入门 X | path.md |
| 整理我的笔记 / 这本书我记住了啥 / 提炼这个主题的划线 | alchemy.md |
| 我今年读了什么 / 季度复盘 / 年度盘点 / 写一篇复盘 | review.md |
| 我现在在读哪本 / 最近在读啥 | 轻量直答（见下方） |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 143 lines · 240 tokens per session scan A 580c3b5f1892
huashu-weread-advisor is a skill published in the GitHub repository alchaincyf/huashu-weread (146 stars, last pushed 18d ago), licensed MIT. It adds 240 tokens to every session and 2,974 once invoked, about $0.0012 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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