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 redfox-data/redfox-community-dsh --skill playlet-wechat-feedgit clone --depth 1 https://github.com/redfox-data/redfox-community-dshWrote 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/redfox-data/redfox-community-dsh/playlet-wechat-feed)<a href="https://agentmods.dev/skills/redfox-data/redfox-community-dsh/playlet-wechat-feed"><img src="https://agentmods.dev/badge/skills/redfox-data/redfox-community-dsh/playlet-wechat-feed/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/redfox-data/redfox-community-dsh/playlet-wechat-feed"><img src="https://agentmods.dev/badge/skills/redfox-data/redfox-community-dsh/playlet-wechat-feed.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00269 | $0.08586 |
| Opus 5 | $0.00134 | $0.04293 |
| Sonnet 5 | $0.00054 | $0.01717 |
| Haiku 4.5 | $0.00027 | $0.00859 |
Grade A, and why
playlet-wechat-feed 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 10d 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 — 567 lines — stays where its author put it; the contents beside it link to each section on GitHub.
短剧-公众号信息源
📝 简介
短剧-公众号信息源是一款专为短剧创作者和内容运营设计的公众号爆款追踪工具,每日自动扫描公众号短剧内容,按阅读量筛选热门作品,智能聚类题材方向后生成可视化 HTML 日报与创作趋势分析。
通过简单的指令,你可以:
- 🔥 发现当日公众号短剧爆款文章
- 📊 获取按题材智能聚类的热门内容
- 📈 分析爆款标题特征与创作趋势
- 🔔 开启每日订阅,自动产出日报
适用于短剧编剧、内容运营、MCN 机构、公众号号主等需要追踪公众号短剧热点的场景。
⚠️ 重要提醒:数据每日 15:00 更新前一天数据（实际可能延迟，以脚本真实探活为准）。查询前脚本先做输入校验：关键词需命中短剧题材词库（
topic_keywords中规定的题材名+全部相关词，如「打脸」命中逆袭题材相关词），命中后直接使用该关键词查询数据；不满足时提醒"关键词不满足查询条件"并推荐相关词，不发起接口请求；日期超出有效查询范围时提醒并自动回退最近有数据日期，无需用户确认。无数据确认制（v2.3）：查询无匹配数据或全量数据不足时，禁止自动发起任何额外查询（禁止自动降级全量、禁止自动扩展题材），必须先向用户展示推荐题材关键词并等待确认，确认后才可按推荐词重新查询。
✨ 功能特性
🎯 核心功能
| 功能模块 | 能力描述 | 核心价值 |
|---|---|---|
| 爆款发现 | 从公众号短剧文章中按阅读量筛选热门内容 | 精准定位高热度短剧作品 |
| 题材聚类 | 自动识别题材方向(穿越/霸总/重生/悬疑等) | 每天题材分类由内容动态决定 |
| 智能查询 | 默认查询全部短剧；数据不足时提示推荐题材并询问用户，确认后才扩展查询 | 节省接口额度,不自动消耗额外查询 |
| 自定义查询 | 用户可指定任意题材/公众号/关键词定向查询；无匹配数据时不自动查询，询问用户是否按推荐词重查 | 灵活覆盖任意短剧细分方向 |
| 创作洞察 | 分析爆款标题特征、题材趋势、公众号表现 | 深度挖掘创作规律 |
| 可视化日报 | 深色主题HTML,封面图+互动数据(为0时隐藏)+文章直链 | 直观展示每日短剧热点 |
| 一键订阅 | --subscribe 开启每日自动产出 |
日报自动攒在本地文件夹 |
✨ 技术亮点
- ⚡ 探活式日期预检:调用前先用轻量请求(无keyword, pageSize=1)真实探测目标日期是否有数据，替代纯本地时钟推断，自动拦截无效查询，避免浪费API额度
- 🧭 前置输入校验:查询前先判断用户的分类/关键词是否命中短剧题材词库（
topic_keywords中规定的题材名+全部相关词，命中后直接使用该关键词查询数据）、日期是否在有效查询范围；不满足时提醒"关键词不满足查询条件"并推荐相关分类和关键词，不发起接口请求 - 🔄 自动回退:
--latest自动向前回退最多7天，找到最近有数据的日期再出日报 - 🛑 无数据确认制:查询无匹配数据 / 全量数据不足时，不自动发起任何额外查询（不自动降级全量、不自动扩展题材），输出推荐题材关键词并等待用户确认，确认后才重新查询
- 🧠 智能聚类:9 大题材自动分类(穿越/霸总/重生/悬疑/甜宠/逆袭/年代/战神/古装)
- 🔒 安全接入:通过 API Key 鉴权,无需账号密码
- 💾 缓存机制:1 小时有效期,避免重复扣费
🔑 安装配置
前置条件
- Python 3 运行环境
- 红狐Hub API Key(前往 红狐Hub 官网 注册获取,格式为
ak_xxxxxxxx)
环境变量配置
数据查询接口通过请求头 X-API-KEY 鉴权,Key 从环境变量 REDFOX_API_KEY 获取。
| 变量名 | 必填 | 说明 |
|---|---|---|
REDFOX_API_KEY |
是 | 红狐Hub API 访问密钥 |
配置方式:
- macOS/Linux:将
export REDFOX_API_KEY=<值>追加到~/.zshrc或~/.bashrc,然后source使其生效 - Windows:
[Environment]::SetEnvironmentVariable("REDFOX_API_KEY", "<值>", "User")(需重启终端) - 配置后验证:
echo $REDFOX_API_KEY(macOS/Linux)或echo %REDFOX_API_KEY%(Windows)
What ships with it
6 files 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.
- 10d ago First seen · 567 lines · 269 tokens per session scan A 65cc1f907db0
playlet-wechat-feed is a skill published in the GitHub repository redfox-data/redfox-community-dsh (5 stars, last pushed yesterday), licensed MIT. It adds 269 tokens to every session and 8,586 once invoked, about $0.0013 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 2026-08-31.
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