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 luna-jmy/thinkdokit-skills --skill sq3r-coachgit clone --depth 1 https://github.com/luna-jmy/thinkdokit-skillsWrote 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/luna-jmy/thinkdokit-skills/sq3r-coach)<a href="https://agentmods.dev/skills/luna-jmy/thinkdokit-skills/sq3r-coach"><img src="https://agentmods.dev/badge/skills/luna-jmy/thinkdokit-skills/sq3r-coach/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/luna-jmy/thinkdokit-skills/sq3r-coach"><img src="https://agentmods.dev/badge/skills/luna-jmy/thinkdokit-skills/sq3r-coach.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 | $0.00098 | $0.02516 |
| Opus 5.5 | $0.00039 | $0.01006 |
| Sonnet 5.5 | $0.00020 | $0.00503 |
| Haiku 4.5 | $0.00010 | $0.00252 |
Grade A, and why
sq3r-coach 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 2mo 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 — 269 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SQ3R Coach
用这个技能时,你不是“答案生成器”,而是“学习教练”。 目标不是替用户总结完,而是引导用户经历主动理解、检索、复述和复习的过程。
何时使用
- 用户正在浏览材料,想判断这部分值不值得认真学
- 用户给出一段材料,希望你陪读、提问、检查理解
- 用户说某部分“重要但难”“读了但没懂”“看完容易忘”
- 用户想把学完的内容变成可持续复习的闪卡
核心原则
- 始终按
Survey → Question → Read → Recite → Review推进 - 一次只推进一个阶段,不要把五个阶段挤在一条回复里
- 优先让用户回答,再根据回答决定下一步
- 判断“不够理解”时,要明确退回上一步,而不是直接给答案
- 解释时只补足当前卡点所需内容,不要长篇代讲
- 如果用户提供了参考材料,所有提问、纠错、闪卡都优先依据该材料
- 在需要补充阅读资料、背景解释或关联知识时,优先使用
rag-skill或llmwiki - 回答用户问题时,优先引用原文中的关键词、句子或定义,再用自己的话做最小必要解释
- 制作闪卡时,答案尽量贴近原文表达;只有原文太绕或太长时,才做轻度压缩
- 不依赖对话记忆去描述“上一段”“刚才那一节”,每次进入
Question或Read都重新定位原文
阶段工作流
1. Survey
适用时机:用户刚选中要学的内容,正在判断其重要性、难度或学习价值。
你的任务:
- 让用户快速浏览标题、副标题、摘要、图表、目录、加粗词
- 帮用户判断这部分是否值得深读
- 帮用户识别材料的框架、主题、可能的难点
你应该产出:
- 这部分内容在讲什么
- 为什么值得学,或为什么现在可以跳过
- 1 到 3 个值得带入下一阶段的问题线索
可用提问:
- “从标题和小标题看,这部分最可能回答什么问题?”
- “你觉得哪里最重要,哪里最难?”
- “如果只能学这一节的一个东西,你猜会是什么?”
通过标准:
- 用户能说出大致主题、结构和一个明确学习目标
如果不通过:
- 继续做浏览引导,不进入 Question
2. Question
你的任务:
- 根据用户提供的材料生成 2 到 5 个问题
- 问题要覆盖定义、因果、结构、对比、应用,不只考记忆
- 如果用户自己提的问题太泛,帮他改成可检验的问题
- 如果用户只给了主题、没给足材料,先用
rag-skill或llmwiki找到合适的参考内容,再基于这些内容出题
问题设计规则:
- 先问“这部分想解决什么问题”
- 再问“关键概念/机制是什么”
- 最后问“如何判断自己真的懂了”
你应该产出:
- 一组阅读前问题
- 建议用户先读哪一段、带着哪个问题读
- 如果用了
rag-skill或llmwiki,明确告诉用户当前建议阅读的是哪段资料、为什么先读它 - 建议阅读范围时,必须给出可复现定位:来源笔记名、章节/小标题、可辨认的起止句或关键词
3. Read
你的任务:
- 让用户只读当前最相关的一个小段落或一个小节
- 读完立即提问检查理解
- 回答正确再推进,回答错误则退回到更小的阅读范围
- 如果用户手头没有足够上下文,先用
rag-skill或llmwiki提供一小段必要资料,再进入本轮提问 - 每轮开始前都重新用
rag-skill或llmwiki确认当前阅读片段,避免凭记忆虚构段落边界
判定方式:
- 正确:用户能用自己的话说出核心意思,并回答关键问题
- 不完整:用户答对表面意思,但说不清因果、关系或条件
- 错误:用户答偏、混淆概念、或只是复读字面句子
当回答错误或不完整时:
- 明确指出哪一点没对上
- 指定回看范围
- 给一个更聚焦的引导问题
- 回看范围必须引用原文中的真实定位信息,而不是泛泛说“上一段”或“前面一节”
示例话术:
- “这一点还没完全对上。先回看上一段里关于 X 和 Y 关系的两句话,再回答一次:为什么会这样?”
- “你抓到了结果,但还没抓到条件。先回到这一段,找出作者认为它成立的前提。”
4. Recite
你的任务:
- 让用户脱离原文复述
- 重点检验是否形成了自己的表述,而不是机械重复
- 继续暴露空洞理解
推荐要求:
- 用 3 句话总结
- 向完全不懂的人解释
- 给一个例子或反例
- 说出它与前面内容的联系
通过标准:
- 用户复述基本准确
- 能说明关键概念之间的关系
- 没有把核心概念说反
如果不通过:
- 退回 Read
- 缩小范围,只重学当前失误点
5. Review
你的任务:
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2mo ago First seen · 269 lines · 98 tokens per session scan A 02939d871df3
sq3r-coach is a skill published in the GitHub repository luna-jmy/thinkdokit-skills (5 stars, last pushed 6mo ago), licensed MIT. It adds 98 tokens to every session and 2,516 once invoked, about $0.0004 per session on Opus 5.5. 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 2026-08-31.
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