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-statsnpx skills add imraywang/wewrite --skill wewrite-statsgit 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-stats)<a href="https://agentmods.dev/skills/imraywang/wewrite/wewrite-stats"><img src="https://agentmods.dev/badge/skills/imraywang/wewrite/wewrite-stats.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 | $0.00088 | $0.00526 |
| Opus 5 | $0.00044 | $0.00263 |
| Sonnet 5 | $0.00018 | $0.00105 |
| Haiku 4.5 | $0.00009 | $0.00053 |
Grade A, and why
wewrite-stats 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 6d 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-stats — 文章数据复盘
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。
执行
读取: {skill_dir}/references/effect-review.md
按其流程执行：fetch_stats.py --days 7 拉数据 → 匹配并回填 history.yaml 的
stats 字段 → 分析最好/最差表现及原因 → 给出后续选题/标题/框架的调整建议。
前置：需要 config.yaml 里的微信 API 凭证。缺凭证 → 告知用户"数据复盘需要配置 公众号 API（config.yaml），当前只能基于 history.yaml 已有记录做定性分析"，然后就 history.yaml 现有内容能分析多少分析多少。刚发布的文章 → 告知等 24h 后再看。
下游影响：回填的 stats 会被选题模块（wewrite-topic）读取——哪种框架/增强策略 表现好会加权到下次推荐。这是数据闭环的一半，另一半是 wewrite-learn 的改稿飞轮。
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.
- 6d ago First seen · 39 lines · 88 tokens per session scan A cafea160a5a3
wewrite-stats is a skill published in the GitHub repository imraywang/wewrite (3,254 stars, last pushed 5d ago), licensed MIT. It adds 88 tokens to every session and 526 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 2026-08-30.
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