Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/HeroAshacker/wechat-content-pipelinenpx agentmods add skills/heroashacker/wechat-content-pipeline/wechat-formatterWrote 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/heroashacker/wechat-content-pipeline/wechat-formatter)<a href="https://agentmods.dev/skills/heroashacker/wechat-content-pipeline/wechat-formatter"><img src="https://agentmods.dev/badge/skills/heroashacker/wechat-content-pipeline/wechat-formatter/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/heroashacker/wechat-content-pipeline/wechat-formatter"><img src="https://agentmods.dev/badge/skills/heroashacker/wechat-content-pipeline/wechat-formatter.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00227 | $0.02372 |
| Opus 5.5 | $0.00091 | $0.00949 |
| Sonnet 5.5 | $0.00045 | $0.00474 |
| Haiku 4.5 | $0.00023 | $0.00237 |
Grade A, and why
wechat-formatter 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 2mo 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.
How it starts
The opening of the file, as written. The whole thing — 253 lines — stays where its author put it; the contents beside it link to each section on GitHub.
微信公众号排版工具 (wechat-formatter v3.0)
Instructions
将 Markdown 转换为微信公众号兼容的内联样式 HTML，支持封面图、增强渲染、信息图、小红书系列图、网页采集和微信发布。
Step 1: 获取输入
- 文件路径:
.md文件 → 直接使用 - 内联文本: 保存为
/tmp/wx-format-temp.md - URL 采集: 使用
scrape子命令先转换为 Markdown - 无输入: 提示用户提供内容
Step 2: 确定参数
基础参数
| 参数 | 默认值 | 说明 |
|---|---|---|
| theme | simple | simple/business/tech 或 JSON 路径 |
| output | 无 | 输出 HTML 文件路径 (-o) |
| clipboard | true | 复制到剪贴板 (-c)，--no-clipboard 禁用 |
| preview | false | 在浏览器中预览 (-p) |
| list-themes | false | 列出所有可用主题 (-l) |
| normalize | false | 中文文本规范化 |
| normalize-full | false | 全量规范化 (引号/标题/空行/列表/标点/间距) |
| important | false | 样式添加 !important |
AI 润色参数
| 参数 | 默认值 | 说明 |
|---|---|---|
| polish | 无 | AI provider: gemini/deepseek/openai/claude |
| polish-type | grammar | grammar/style/title/structure/deai/readability/summary/seo |
| api-key | 无 | AI API Key |
| dry-run | false | 仅打印 prompt，不调用 API |
| prompt-file | 无 | 自定义 prompt 文件路径 |
| polish-only | false | 仅润色，输出 Markdown |
封面图参数
| 参数 | 默认值 | 说明 |
|---|---|---|
| cover | false | 生成 AI 封面图 |
| cover-style | 无 | hero/conceptual/typography/metaphor/minimal |
| cover-palette | 无 | warm/cool/dark/vivid/pastel/mono |
| cover-ratio | 2.35:1 | 2.35:1/16:9/1:1 |
配图参数
| 参数 | 默认值 | 说明 |
|---|---|---|
| images | false | 生成 AI 配图 (Gemini) |
| image-model | gemini-3-pro-image-preview | 图像生成模型 |
| image-density | balanced | minimal/balanced/rich |
| image-style | 无 | notion/warm/minimal/blueprint/watercolor |
增强渲染参数
| 参数 | 默认值 | 说明 |
|---|---|---|
| math | false | KaTeX 数学公式渲染 ($..$ 行内, $$...$$ 块级) |
| mermaid | false | Mermaid 图表渲染 |
| toc | false | 生成目录导航 |
发布参数
| 参数 | 默认值 | 说明 |
|---|---|---|
| publish | 无 | 发布目标平台 (wechat) |
| publish-method | api | 发布方式 |
| publish-title | 无 | 文章标题 (可从 frontmatter 读取) |
| publish-author | 无 | 文章作者 |
| draft-only | true | 仅保存为草稿 |
Step 3: 执行转换
基础排版：
cd ~/Claude/🧪\ 小项目与测试/wx-format
node index.js <input.md> -t <theme> -o <output.html> --no-clipboard [--normalize] [--important]
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.
- 2mo ago First seen · 253 lines · 227 tokens per session scan A ec35280677de
wechat-formatter is a skill published in the GitHub repository HeroAshacker/wechat-content-pipeline (12 stars, last pushed 8mo ago), licensed MIT. It adds 227 tokens to every session and 2,372 once invoked, about $0.0009 per session on Opus 5.5. 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.
wechat-formatter compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| wechat-formatterHeroAshacker/wechat-content-pipeline | Skill | 12 | 8mo ago | MIT | A | 227 | Claude Code |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |