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 agentmods add skills/guoqiaozhou/study-with-claude-code/compoundnpx skills add guoqiaoZhou/study-with-claude-code --skill compoundgit clone --depth 1 https://github.com/guoqiaoZhou/study-with-claude-codeWrote 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/guoqiaozhou/study-with-claude-code/compound)<a href="https://agentmods.dev/skills/guoqiaozhou/study-with-claude-code/compound"><img src="https://agentmods.dev/badge/skills/guoqiaozhou/study-with-claude-code/compound.svg" alt="Measured on agentmods" 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 | $0.00064 | $0.01303 |
| Opus 5 | $0.00032 | $0.00651 |
| Sonnet 5 | $0.00013 | $0.00261 |
| Haiku 4.5 | $0.00006 | $0.00130 |
Grade A, and why
swcc-compound 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 5d 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
swcc · compound — 学习复盘 + 让系统更懂你
这不是统计报表(统计看 /swcc-stats)。compound 做一件会复利的事:通过和你聊"学得怎么样、哪里吃力、为什么",提炼出关于「你怎么学」的认识,沉淀进学习者档案,从而让以后所有技能把你教得越来越好。
开始前先读数据契约:
${CLAUDE_PLUGIN_ROOT}/skills/_shared/data-contract.md(第十四节学习者档案)。本技能更新全局learner-profile.md,可选写一份可读复盘;不碰 progress/tree。
参数:$ARGUMENTS —— 可选 topic(默认 activeTopic;反思可跨近期记录)。
核心原则
- 复利在"怎么教你",不在统计。 产出是更准的学习者档案(教学层:讲解偏好 + 反复盲区 + 有效引导),它会回流调教每个技能。统计交给 stats。
- 先聊感受,再提炼。 不要只机械分析记录。和用户对话,挖出他学习时的真实感受与背后原因,再从中提炼——这才是有价值的信号来源。
- 宁缺毋滥、可微调。 只在有反复出现的真实信号时才更新档案;偶发现象不进;旧条目过时就改/删,不是只追加。
- 透明可否决。 每次对档案的改动都展示给用户、可被编辑或拒绝。它在"猜你",你要能纠正。
- 只调教学层。 不往档案塞目标、题目、知识点(那是 daily/weak/knowledge-system)。只放跨学科、关于"你这个人"的东西。
流程
1. 收集素材
- 读近期
review-sessions/+mock-sessions/(取该 topic;也可参考本轮对话)。重点不是分数,是模式:哪些概念反复卡、哪种题型更弱、哪种讲法之后掌握得更好。 - 读现有
learner-profile.md(若有)作为基线——这次是"修订",不是从零写。
2. 反思对话（关键，先聊再提炼）
和用户聊几个开放问题,挖真实感受与根因,例如:
- 这段时间学下来,哪块最吃力 / 最有成就感?
- 有没有"明明讲过/复习过还是没记住"的?当时是什么感觉、卡在哪?
- 哪种讲解方式让你最快"想通"?哪种你听着就走神?
- 一题一题问,顺着用户回答往下挖根因(是兴趣?是讲法不对路?是某类思维习惯?),别停在表面。
提醒(原则 2):用户一句"就是记不住"背后,可能是"被动听、没追问原理"或"缺具体例子"——挖到这一层,才提炼得出有用的档案条目。
3. 提炼 → 更新学习者档案
- 从记录模式 + 反思对话里,提炼跨学科、关于人的条目,归入三类:讲解偏好 / 反复盲区 / 有效引导。
- 对照基线:新增(有反复信号才加)、修订(旧条目更准了就改)、删除(过时/被推翻的)。宁缺毋滥。
- 把拟改动展示给用户确认/编辑,再写回
$HOME/.study-with-cc/learner-profile.md(按数据契约第十四节格式)。无实质新信号 → 明说"这次没有值得入档的新发现",不硬凑。
4. 给一份简短可读复盘（给人看,非统计）
口头(或可选写入 reports/<date +%F>.md)给用户一段话:这段时间你在哪些地方真的进步了、哪类东西仍是你的软肋(指学习方式层面,不是列薄弱知识点)、下一步在"怎么学"上可以怎么调。不画统计曲线(那是 stats)。
5. 输出摘要
🪞 学习复盘完成
🧠 学习者档案更新：<新增 a 条 / 修订 b 条 / 无变化>
近期模式：<一句话——关于你怎么学的最大发现>
下一步(学习方式上)：<一句话建议>
（统计数字见 /swcc-stats）
质量基准
- 档案更新是关于"怎么教你"的跨学科洞察,不是知识点清单、不是目标、不是统计。
- 经过了反思对话挖根因,不是只读记录拍脑袋;改动给用户确认过。
- 宁缺毋滥:没有真实信号时不硬加;progress/tree 未动。
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.
- 5d ago First seen · 66 lines · 64 tokens per session scan A 77e6173d5b0b
swcc-compound is a skill published in the GitHub repository guoqiaoZhou/study-with-claude-code (2 stars, last pushed 2mo ago), licensed MIT. It adds 64 tokens to every session and 1,303 once invoked, about $0.0003 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 2026-08-31.
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