swcc-blog

swcc-blog is a skill for Claude Code from guoqiaoZhou/study-with-claude-code. It costs 72 tokens per session (1,239 once invoked), scanned A, original, MIT.

A writing workflow that turns the learning and discussion from the current conversation into one or more readable blog articles.

In plain words
What is it for?
Use it to create one or several articles from the conversation, with an optional split and output directory.
Why use it?
It preserves useful explanations, examples, and conclusions in files that people can read or publish, instead of leaving them only in the chat.

Skill for Claude Code

Written for Claude Code: user-invocable in frontmatter. Also seen: names the TodoWrite tool.

Part of the study-with-claude-code plugin — 12 skills shipped together

Good fit Use it to create one or several articles from the conversation, with an optional split and output directory.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/guoqiaozhou/study-with-claude-code/blog
Install

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.

Any agent
npx skills add guoqiaoZhou/study-with-claude-code --skill blog
Clone the repo
git clone --depth 1 https://github.com/guoqiaoZhou/study-with-claude-code

Made for: Claude Code.

Or install study-with-claude-code, the plugin that ships this one along with the rest of its 12 skills.

Wrote 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.

agentmods badge for swcc-blog

README.md
[![agentmods](https://agentmods.dev/badge/skills/guoqiaozhou/study-with-claude-code/blog.svg)](https://agentmods.dev/skills/guoqiaozhou/study-with-claude-code/blog)
Your own site
<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>
Per session 72 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,239 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 7d ago against content hash 697ff89af614, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

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.

skills/blog/SKILL.md · 87 lines

What it actually says

swcc · blog — 把本轮学习/讨论沉淀成 blog

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

  1. 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。
  2. 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。
  3. 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。
  4. 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
  5. 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

  • 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。
Changes

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.

  1. 7d ago First seen · 87 lines · 72 tokens per session scan A 697ff89af614

Subscribe to this mod's changes

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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