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/heroashacker/wechat-content-pipeline/wechat-topicnpx skills add HeroAshacker/wechat-content-pipeline --skill wechat-topicgit 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-topic)<a href="https://agentmods.dev/skills/heroashacker/wechat-content-pipeline/wechat-topic"><img src="https://agentmods.dev/badge/skills/heroashacker/wechat-content-pipeline/wechat-topic.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 | $0.00082 | $0.00847 |
| Opus 5 | $0.00041 | $0.00424 |
| Sonnet 5 | $0.00016 | $0.00169 |
| Haiku 4.5 | $0.00008 | $0.00085 |
Grade A, and why
wechat-topic 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
微信公众号选题工具 (wechat-topic)
Instructions
聚合多平台热点数据，辅助公众号选题决策。
Step 1: 确定参数
| 参数 | 默认值 | 说明 |
|---|---|---|
| niche | 无 | 领域关键词过滤 (逗号分隔) |
| sources | 全部 | 数据源: weibo,zhihu,baidu,36kr |
| count | 20 | 显示条数 |
| analyze | false | 启用 AI 选题分析 |
| provider | gemini | AI provider |
| api-key | 无 | AI API Key |
| dry-run | false | 仅打印分析 prompt |
| json | false | JSON 格式输出 |
| output | 无 | 结果保存到文件路径 |
| format | table | 输出格式: table (默认) / structured (结构化 JSON) |
Step 2: 执行
cd ~/Claude/🧪\ 小项目与测试/wx-format
node index.js topic [--niche <keywords>] [--sources <list>] [--count <n>] [--analyze] [--output <path>] [--format structured]
Step 3: 返回结果
- 展示热点列表（序号、来源、热度、标题）
- 如启用 AI 分析，展示 5 个推荐选题（标题+角度+评分+理由）
Examples
Example 1: AI 领域热点 + 分析
输入: 用户想了解 AI 领域的微博/知乎热点并获取选题建议
命令:
cd ~/Claude/🧪\ 小项目与测试/wx-format
node index.js topic --niche "AI,人工智能" --sources weibo,zhihu --analyze
输出: 热点列表 + 5 个 AI 推荐选题（标题+角度+评分+理由）
Example 2: Dry-run 预览分析 prompt
输入: 用户想查看 AI 分析的 prompt 而不消耗 API 额度
命令:
node index.js topic --niche "AI" --analyze --dry-run
输出: 打印完整分析 prompt 到终端
Example 3: JSON 输出用于后续处理
命令:
node index.js topic --json --count 10
输出: JSON 格式的热点数据，可供脚本或工作流消费
Example 4: 结构化输出供管线消费
命令:
node index.js topic --niche "AI" --analyze --format structured --output /tmp/topic.json
输出: 结构化 JSON 保存到文件，包含 topics 数组和 analyzed 推荐
Error Handling
- 网络超时 → 跳过失败源，显示可用数据
- AI 分析失败 → 检查
--api-key或环境变量，用--dry-run验证 prompt - 数据源为空 → 尝试切换
--sources，部分平台可能临时不可用
Related Skills
wechat-formatter- 选题确定后排版发布wechat-writer- 选题确定后 AI 写作wechat-pipeline- 选题→写作→排版全流程
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 · 97 lines · 82 tokens per session scan A 58f282e02e87
wechat-topic is a skill published in the GitHub repository HeroAshacker/wechat-content-pipeline (11 stars, last pushed 6mo ago), licensed MIT. It adds 82 tokens to every session and 847 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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