WeWrite is a workflow for creating and publishing WeChat public-account articles with an AI coding agent, covering topic selection, source gathering, drafting, review, optional illustrations, formatting, and draft delivery. It is for public-account writers who want to produce articles or adapt them for other platforms, and its catalogue entries provide the skills and plugin for running those workflow steps.
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/imraywang/wewrite/wewrite-learnnpx skills add imraywang/wewrite --skill wewrite-learngit clone --depth 1 https://github.com/imraywang/wewriteWrote 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/imraywang/wewrite/wewrite-learn)<a href="https://agentmods.dev/skills/imraywang/wewrite/wewrite-learn"><img src="https://agentmods.dev/badge/skills/imraywang/wewrite/wewrite-learn.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 | $0.00110 | $0.00691 |
| Opus 5 | $0.00055 | $0.00345 |
| Sonnet 5 | $0.00022 | $0.00138 |
| Haiku 4.5 | $0.00011 | $0.00069 |
Grade A, and why
wewrite-learn 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
wewrite-learn — 自学习（改稿飞轮 / 范文库 / 排版学习）
运行约定
- CLI：确定性操作走
wewrite命令（需在 PATH；缺失则引导uv tool install wewrite，或在仓库里bash install.sh）。 - {home}：用户状态目录 =
$WEWRITE_HOME或~/.wewrite（wewrite home可查）。config/style/history/playbook/output/exemplars 全在 {home}，不在仓库；references 文档中的状态路径同此约定。 读取: <路径>= 用文件读取工具真实读完该文件再继续，不是注释。- references/：本 skill 自带
{skill_dir}/references/；references 文档内的{skill_dir}即本 skill 目录。
子功能分发
| 用户说 | 动作 |
|---|---|
| 学习我的修改 / 我改了，学习一下 | 读取: {skill_dir}/references/learn-edits.md，按其流程执行。支持本地 markdown 修改与微信草稿箱同步（wewrite learn-edits --from-wechat） |
| 学习排版 / 学排版 + URL | wewrite learn-theme <url> --name <name>，提取后提示用户设置 style.yaml 的 theme 字段 |
| 学习这篇文章 / 导入范文 + URL | wewrite fetch-article <url> -o /tmp/article.md && wewrite exemplar /tmp/article.md -s <账号名>；默认第三方 |
| 导入范文 + 本地文件 | wewrite exemplar <文件路径>；用户明确为本人文章时加 --user-authored |
| 查看范文库 | wewrite exemplar --list |
范文库的用途：exemplars 会在写作模块（wewrite-write）按框架类型注入初稿 prompt， 只用于校准结构和节奏，不能复用其中的观点、句子或个人经历。导入完成后告知用户库里现有 多少篇、覆盖哪些 category 和所有权标记。
改稿飞轮的价值：每次学习让下一篇初稿更接近用户风格。learn-edits.md 的 只有重复出现或用户明确确认的同范围规则才会成为硬约束；单次修改始终只是软参考。
What ships with it
1 file 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.
- 5d ago First seen · 43 lines · 110 tokens per session scan A e06f303b2654
wewrite-learn is a skill published in the GitHub repository imraywang/wewrite (3,230 stars, last pushed 4d ago), licensed MIT. It adds 110 tokens to every session and 691 once invoked, about $0.0006 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 2026-08-30.
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