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 guoqiaoZhou/study-with-claude-code --skill bloggit 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/blog)<a href="https://agentmods.dev/skills/guoqiaozhou/study-with-claude-code/blog"><img src="https://agentmods.dev/badge/skills/guoqiaozhou/study-with-claude-code/blog.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 5.1 | $0.00072 | $0.01239 |
| Opus 5 | $0.00036 | $0.00620 |
| Sonnet 5 | $0.00014 | $0.00248 |
| Haiku 4.5 | $0.00007 | $0.00124 |
Grade A, and why
swcc-blog scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
swcc · blog — 把本轮学习/讨论沉淀成 blog
把当前这轮对话里学到的、问到的、澄清的内容,合成 1 篇或多篇可读的 blog 文章,写到用户可见的目录(默认当前工作目录,便于 git/发布)。
本技能读当前对话(主) + 可选地读该专题 knowledge-system 作背景,只写 blog 文件到工作目录,不碰 progress/tree。
参数:$ARGUMENTS —— 可选拆分提示(如 3、按章节、每个主题一篇)、可选 out:<目录>(默认 ./blogs/)。
核心原则
- 沉淀讨论的增量,不是复述提纲。 最值钱的是本轮对话里澄清出的理解、踩过的坑、想通的点——blog 要把这些写进去,而不是照抄 knowledge-system。
- blog 级可读文章。 有导语、有展开、有例子、有小结;是给人读的文章,不是 bullet 日志。
- 拆分以用户为准。 用户说几篇/怎么拆就照办;没说时先提议一个拆法让用户确认,再写。
- 写到用户可见处。 默认
./blogs/(当前目录),可被out:或用户口头指定覆盖;不埋进插件数据目录。 - 不碰 progress/tree。 纯内容产出。
与相邻技能的区别
| 命令 | 产出 | 给谁看 |
|---|---|---|
| stop | review-session 记录(评分/薄弱点) | 给系统记账 |
| compound | reports/ 趋势报告 | 看进度趋势 |
| deep | deep-notes/ 单概念横向拓展 | 拓宽视野 |
| blog | 可读 blog 文章(本轮学习+讨论) | 给人读 / 发布 |
流程
1. 框定来源与范围
- 回顾当前对话:本轮学了/讨论了/澄清了哪些主题、达成了哪些有价值的理解与结论。
- 本轮没有可成文的实质学习/讨论 → 告知用户「本轮没有可成文的内容」,停止。
2. 定拆分
- 用户给了篇数或边界(如「3 篇」「按章节」「每个主题一篇」)→ 照办。
- 没给 → 先提议一个拆法(例:「本轮覆盖了 A / B / C 三块,建议拆 3 篇,分别讲…;或合成 1 篇综述。你要哪种?」)让用户确认或调整,再写。
3. 定输出目录
- 有
out:<目录>或用户口头指定 → 用它;否则默认./blogs/(当前工作目录)。写前mkdir -p。
4. 写 blog(每篇一个文件,长文分块)
- 每篇文件名 = 文章标题的小写 kebab-case(如
./blogs/<title>.md)。 - 多篇 → 用 TodoWrite 每篇一个 todo,逐篇写、逐篇勾;单篇过长也按数据契约第十二节先骨架后分块追加,避免超时。
- 每篇结构(可按主题调整):
# <文章标题> > <一句话导语:这篇讲什么、读完能得到什么> ## 背景 / 要解决的问题 <为什么要搞清这个> ## 正文 <把对话里讲透/澄清的内容写成连贯讲解,配具体例子;该深则深> ## 容易踩的坑 / 澄清 <本轮对话里纠正过的误解、边界条件、设计取舍> ## 小结 <要点回顾 / 一句话带走> - 内容以本轮对话为主,用自身知识补全连贯性,可选地引 knowledge-system 作背景。忠实于讨论里实际达成的理解,不要塞没讨论过的泛泛内容。
5. 输出摘要
📝 已生成 <n> 篇 blog
· <title-1> → <路径>
· <title-2> → <路径>
质量基准
- 每篇是连贯可读的文章,体现了本轮讨论/澄清的增量,不是 knowledge-system 的复述。
- 拆分符合用户意图(或经用户确认)。
- 写到了用户指定/默认的可见目录;progress/tree 未动;多篇/长文分块写、未超时。
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.
- 7d ago First seen · 87 lines · 72 tokens per session scan A 697ff89af614
swcc-blog is a skill published in the GitHub repository guoqiaoZhou/study-with-claude-code (2 stars, last pushed 2mo ago), licensed MIT. It adds 72 tokens to every session and 1,239 once invoked, about $0.0004 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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