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 malue-ai/dazee-small --skill reading-companiongit clone --depth 1 https://github.com/malue-ai/dazee-smallWrote 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/malue-ai/dazee-small/reading-companion)<a href="https://agentmods.dev/skills/malue-ai/dazee-small/reading-companion"><img src="https://agentmods.dev/badge/skills/malue-ai/dazee-small/reading-companion/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/malue-ai/dazee-small/reading-companion"><img src="https://agentmods.dev/badge/skills/malue-ai/dazee-small/reading-companion.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 | $0.00026 | $0.00767 |
| Opus 5 | $0.00013 | $0.00383 |
| Sonnet 5 | $0.00005 | $0.00153 |
| Haiku 4.5 | $0.00003 | $0.00077 |
Grade A, and why
reading-companion 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 9d 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.
What it actually says
阅读伴侣
帮助用户管理读书笔记：提取核心观点、跨书籍主题关联、生成复习卡片。
使用场景
- 用户说「帮我整理这本书的核心观点」「总结一下关键笔记」
- 用户说「这本书和上次那本有什么关联」「这个主题下我读过哪些书」
- 用户说「帮我做读书笔记的复习卡片」「推荐下一本读什么」
执行方式
直接使用 LLM 能力分析和整理，读书笔记存储在本地文件中。
读书笔记模板
# 《[书名]》读书笔记
**作者**: [作者名]
**阅读日期**: YYYY-MM-DD
**评分**: ⭐⭐⭐⭐ (4/5)
## 一句话总结
[用一句话概括这本书的核心]
## 核心观点 (Top 3-5)
1. [观点 1] — [简要解释]
2. [观点 2] — [简要解释]
3. [观点 3] — [简要解释]
## 金句摘录
> "[原文摘录]" — p.XX
> 我的理解：[个人思考]
## 实践行动
- [ ] [读完这本书我要做什么]
## 关联书籍
- 《[相关书名]》: [关联点]
笔记存储
~/Documents/xiaodazi_reading/
├── books/
│ ├── thinking-fast-and-slow.md
│ ├── atomic-habits.md
│ └── ...
├── themes/
│ ├── productivity.md # 主题汇总
│ └── decision-making.md
└── flashcards/
└── review-cards.md
跨书籍主题关联
当用户读完多本书后，自动发现主题关联：
## 主题: 习惯养成
### 相关书籍
1. 《Atomic Habits》 — 习惯的四步法则
2. 《The Power of Habit》 — 习惯回路
3. 《Thinking, Fast and Slow》 — 系统 1 与自动化行为
### 核心洞察
不同作者从不同角度论证了...
### 综合行动建议
结合多本书的建议，最有效的做法是...
复习卡片
Q: 《Atomic Habits》的四步法则是什么？
A: Cue → Craving → Response → Reward
Q: Daniel Kahneman 的"系统 1"和"系统 2"有什么区别？
A: 系统 1: 快速、直觉、自动；系统 2: 慢速、分析、费力
输出规范
- 笔记保存到本地
~/Documents/xiaodazi_reading/目录 - 核心观点精炼到 3-5 个
- 金句摘录标注页码或章节
- 复习卡片使用 Q/A 格式
- 主题关联至少涉及 2 本书
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.
- 9d ago First seen · 105 lines · 26 tokens per session scan A f23ebf4d2f59
reading-companion is a skill published in the GitHub repository malue-ai/dazee-small (36 stars, last pushed 5mo ago), licensed MIT. It adds 26 tokens to every session and 767 once invoked, about $0.0001 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 2026-09-03.
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