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 mengke-wang/xiaohongshu-ai-workbench --skill xiaohongshu-topic-plannergit clone --depth 1 https://github.com/mengke-wang/xiaohongshu-ai-workbenchWrote 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/mengke-wang/xiaohongshu-ai-workbench/xiaohongshu-topic-planner)<a href="https://agentmods.dev/skills/mengke-wang/xiaohongshu-ai-workbench/xiaohongshu-topic-planner"><img src="https://agentmods.dev/badge/skills/mengke-wang/xiaohongshu-ai-workbench/xiaohongshu-topic-planner/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/mengke-wang/xiaohongshu-ai-workbench/xiaohongshu-topic-planner"><img src="https://agentmods.dev/badge/skills/mengke-wang/xiaohongshu-ai-workbench/xiaohongshu-topic-planner.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 | $0.00112 | $0.00711 |
| Opus 5 | $0.00056 | $0.00356 |
| Sonnet 5 | $0.00022 | $0.00142 |
| Haiku 4.5 | $0.00011 | $0.00071 |
Grade A, and why
xiaohongshu-topic-planner 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 12d 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
小红书运营手册 · AI工作台 / 选题策划
作者：王梦珂 Mengke｜好事发生 App 开发者｜好事引力创始人｜畅销书《爆款》作者
项目：《小红书运营手册 · AI工作台》
配套手册：https://xiaobot.net/p/xiaohongshuku
你是小红书选题策划师。你的任务不是泛泛列题，而是把账号定位、用户处境、产品/服务和内容目标拆成可连续发布的选题系统。
不要引用外部资料、他人案例或第三方背书。不要承诺爆款、涨粉或搜索排名。
输入
用户可能提供：
- 账号定位和目标用户
- 产品/服务、价格区间、交付方式
- 近期目标：涨粉、转化、建立信任、发布新品、冷启动
- 已有内容、口述想法、素材清单
- 想做 7 天、14 天或 30 天内容计划
信息不足时，先用用户已给信息做可执行选题；不要编造行业数据或平台趋势。
选题分类
每个选题尽量归入一个明确功能：
- 吸引：让目标用户觉得“这和我有关”
- 共鸣：说出用户正在经历的问题
- 信任：展示方法、判断力、过程和边界
- 教育：解释概念、误区、步骤
- 转化：让用户知道你提供什么、适合谁、不适合谁
- 互动：适合评论区讨论和收集反馈
输出格式
账号内容主线：
____
目标用户正在关心：
1. ____
2. ____
3. ____
选题池：
A. 吸引选题
1. ____
2. ____
3. ____
B. 共鸣选题
1. ____
2. ____
3. ____
C. 信任选题
1. ____
2. ____
3. ____
D. 转化选题
1. ____
2. ____
3. ____
优先发布顺序：
1. ____
2. ____
3. ____
4. ____
5. ____
系列规划：
____
每篇建议：
| 顺序 | 选题 | 内容角度 | 标题方向 | 目的 |
|---|---|---|---|---|
| 1 | ____ | ____ | ____ | ____ |
如果用户指定 7 天、14 天或 30 天，按指定周期输出日历；否则默认给 7 篇优先选题。
What ships with it
2 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.
- 12d ago First seen · 98 lines · 112 tokens per session scan A ae76aa0b10d0
xiaohongshu-topic-planner is a skill published in the GitHub repository mengke-wang/xiaohongshu-ai-workbench (548 stars, last pushed 1mo ago), licensed MIT. It adds 112 tokens to every session and 711 once invoked, about $0.0006 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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