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 HeroAshacker/wechat-content-pipeline --skill wechat-content-routergit clone --depth 1 https://github.com/HeroAshacker/wechat-content-pipelineWrote 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-content-router)<a href="https://agentmods.dev/skills/heroashacker/wechat-content-pipeline/wechat-content-router"><img src="https://agentmods.dev/badge/skills/heroashacker/wechat-content-pipeline/wechat-content-router/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-content-router"><img src="https://agentmods.dev/badge/skills/heroashacker/wechat-content-pipeline/wechat-content-router.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 | $0.00160 | $0.02054 |
| Opus 5 | $0.00080 | $0.01027 |
| Sonnet 5 | $0.00032 | $0.00411 |
| Haiku 4.5 | $0.00016 | $0.00205 |
Grade A, and why
wechat-content-router 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 9d 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 — 180 lines — stays where its author put it; the contents beside it link to each section on GitHub.
微信公众号内容套件路由器 (wechat-content-router)
版本: v1.0.0 | 创建日期: 2026-02-28
Instructions
Step 1: 意图分析
解析用户输入，识别核心意图：
| 意图类别 | 识别信号 | 置信度阈值 |
|---|---|---|
| 全流程创作 | "帮我写一篇文章"、"一键写文"、"从想法到发布" | 90% |
| 排版美化 | "排版"、"格式化"、"转微信HTML" | 95% |
| AI写作 | "写大纲"、"扩写观点"、"生成文章" | 95% |
| 热点选题 | "热点"、"选题"、"趋势分析" | 95% |
| 质量评估 | "评分"、"评估"、"质量门禁" | 95% |
| 封面图生成 | "封面图"、"cover"、"封面" | 90% |
| 信息图生成 | "信息图"、"infographic"、"数据可视化" | 90% |
| 小红书系列图 | "小红书"、"xhs"、"红书图" | 90% |
| 网页采集 | "采集"、"scrape"、"URL转Markdown" | 90% |
| 微信发布 | "发布"、"publish"、"草稿" | 90% |
| X/Twitter采集 | "推文"、"X内容"、"Twitter" | 90% |
Step 2: 路由匹配
根据意图分析结果，匹配目标 SKILL：
| 用户意图 | 目标 SKILL | 调用方式 |
|---|---|---|
| 全流程创作 | wechat-pipeline |
/wechat-pipeline "主题" |
| 排版美化 | wechat-formatter |
/wechat-formatter |
| AI写作 | wechat-writer |
/wechat-writer |
| 热点选题 | wechat-topic |
/wechat-topic |
| 质量评估 | wechat-article-evaluator |
/wechat-article-evaluator |
| 封面图生成 | wechat-formatter |
/wechat-formatter (cover子功能) |
| 信息图生成 | wechat-formatter |
/wechat-formatter (infographic子命令) |
| 小红书系列图 | wechat-formatter |
/wechat-formatter (xhs子命令) |
| 网页采集 | wechat-formatter |
/wechat-formatter (scrape子命令) |
| 微信发布 | wechat-formatter |
/wechat-formatter (--publish) |
| X/Twitter采集 | x-to-markdown |
/x-to-markdown (utility-suite) |
Step 3: 分派执行
- 单一意图: 直接调用匹配的 SKILL
- 复合意图: 拆解为步骤，按依赖顺序调用多个 SKILL
- 模糊意图: 置信度 < 80% 时，向用户确认意图后再分派
复合意图处理示例:
- "写一篇文章并排版" → wechat-writer → wechat-formatter
- "选题后写文章" → wechat-topic → wechat-writer
- "全流程写作" → wechat-pipeline (内部已编排全流程)
错误处理
| 场景 | 处理方式 |
|---|---|
| 意图不明确 | 列出可用 SKILL，请用户选择 |
| SKILL 未激活 | 提示用户激活对应 SKILL |
| 跨套件路由 | 检查目标 SKILL 是否可用 |
| 多意图冲突 | 优先处理前置依赖，再处理后续步骤 |
Examples
Example 1: 全流程创作
输入: "帮我写一篇关于AI教育的公众号文章"
路由决策:
- 意图: 全流程创作
- 置信度: 92%
- 目标: wechat-pipeline
执行: /wechat-pipeline "AI教育趋势" --type opinion
Example 2: 排版美化
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.
- 9d ago First seen · 180 lines · 160 tokens per session scan A 0ef1a67ab341
wechat-content-router is a skill published in the GitHub repository HeroAshacker/wechat-content-pipeline (11 stars, last pushed 6mo ago), licensed MIT. It adds 160 tokens to every session and 2,054 once invoked, about $0.0008 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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