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 chengkj99/kj-skills --skill wechat-companiongit clone --depth 1 https://github.com/chengkj99/kj-skillsWrote 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/chengkj99/kj-skills/wechat-companion)<a href="https://agentmods.dev/skills/chengkj99/kj-skills/wechat-companion"><img src="https://agentmods.dev/badge/skills/chengkj99/kj-skills/wechat-companion/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/chengkj99/kj-skills/wechat-companion"><img src="https://agentmods.dev/badge/skills/chengkj99/kj-skills/wechat-companion.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 | $0.00149 | $0.01656 |
| Opus 5 | $0.00075 | $0.00828 |
| Sonnet 5 | $0.00030 | $0.00331 |
| Haiku 4.5 | $0.00015 | $0.00166 |
Grade A, and why
wechat-companion 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.
How it starts
The opening of the file, as written. The whole thing — 149 lines — stays where its author put it; the contents beside it link to each section on GitHub.
公众号配套物料生成技能
为已有或即将发布的公众号文章,一次性生成所有配套运营物料,开箱即用,无需反复修改。
触发条件
用户提供:
- 文章主题 / 标题(必填):如「Claude Code 完全新手指南」「如何用 AI 10倍提速写代码」
- 文章核心内容摘要(可选):粘贴文章大纲、核心章节或几句话描述,提升物料精准度
- 目标读者(可选):如「程序员」「产品经理」「刚入门 AI 的普通人」,默认为「对 AI 编程感兴趣的程序员」
若用户只提供主题名(如来自 tutorial-guide 的教程主题),可以推断文章类型、目标读者和通用收获,无需追问。不得推断或补造文章未提供的亲历、功能细节、数据、用户反馈和效果。
输出规范
按固定顺序输出以下 5 个模块,每个模块用 --- 分隔,方便复制粘贴:
模块 1:公众号文章前言
- 字数:150–300 字
- 风格:说人话、有共鸣感,从读者痛点或反常识切入,禁止「本文将介绍……」开头
- 结构:1–2 句钩子 → 2–3 句背景/痛点 → 1 句引导读者继续读
- 语气:像朋友分享干货,不像教科书,无 AI 腔(禁止「不难发现」「值得注意」「综上所述」)
模块 2:5 个公众号标题
- 每个标题 20 字以内
- 风格多样:至少覆盖「数字型」「痛点型」「反常识型」「实用型」「身份认同型」各 1 个
- 要有传播欲:让读者看到就想转发或收藏
- 每个标题附 1 句简短说明(为何选这个角度)
模块 3:120 字以内摘要
- 严格控制在 120 字以内(含标点)
- 用于公众号摘要栏、朋友圈引用展示
- 一句话说清:这篇文章是什么、解决什么问题、读完能得到什么
- 不要废话,每个字都有信息量
模块 4:封面图生成提示词
- 输出一段英文 prompt,用于 Midjourney / Stable Diffusion / DALL-E 等工具生成封面图
- 比例:2.35:1(横版宽幅,公众号封面标准)
- 提示词要求:
- 风格清晰(如 flat design / tech illustration / dark theme 等)
- 包含主题相关视觉元素(如 AI、代码、工具图标等)
- 包含色调/氛围描述
- 末尾附上比例参数:
--ar 47:20(等价于 2.35:1)
- 同时输出一句中文说明,解释这个 prompt 的视觉方向
模块 5:转发文案
输出两段文案,格式如下:
朋友圈文案(100 字以内):
- 可以用第一人称表达当下的个人观点;只有原文或用户素材支持时才写个人经历和感受
- 可含 1–2 个 emoji,不要滥用
- 结尾自然引导点击,不要「速速转发」「强烈推荐」等硬推
AI 实战交流群文案(80 字以内):
- 更直接、社群感强
- 突出「实用 / 能用 / 现在就能上手」
- 可含群友常用语气,如「分享一个」「这个真的很好用」
写作约束(全局)
- 禁止 AI 腔:不用「深度解析」「全面梳理」「系统讲解」「一文搞懂」作为核心卖点词
- 禁止爹味:不用「你必须」「切记」,改用说明适用条件和理由的建议,如「在 X 情况下,更稳妥的做法是 Y」。「我的习惯是」「我踩过这个坑」只能在素材明确支持时使用。
- 禁止虚构:不得为了“像真人”补造第一人称经历、对话、测试过程、耗时/次数、数据、用户反馈或效果。文章未提供的事实不写进标题、摘要或转发文案。
- 禁止套话:每个模块都要针对具体主题定制,不输出通用模板填空结果
- 中文输出:除封面图 prompt 外,全部用中文
执行流程
- 解析输入:提取主题、核心内容、目标读者
- 推断文章定位:这是入门教程 / 实战案例 / 工具评测 / 方法论分享?决定物料基调
- 逐模块生成:按顺序输出,每个模块独立且完整
- 输出后自检:
- 前言有没有「本文将介绍」开头?→ 有则重写
- 5 个标题类型有没有重复?→ 重复则替换
- 摘要超过 120 字?→ 压缩
- 封面 prompt 有没有
--ar 47:20?→ 没有则补上 - 朋友圈文案是否像真人写的?→ AI 腔则重写
- 标题、摘要和转发文案中的亲历、数据、反馈和效果是否能回到原文?→ 不能则删除或改为客观表述
输出示例结构
【模块 1:前言】
...前言内容...
---
【模块 2:5 个标题】
① [标题](说明)
② [标题](说明)
③ [标题](说明)
④ [标题](说明)
⑤ [标题](说明)
---
【模块 3:120 字摘要】
...摘要内容...(已控制在 120 字以内)
---
【模块 4:封面图提示词】
[英文 prompt] --ar 47:20
[中文视觉说明]
---
【模块 5:转发文案】
**朋友圈:**
...
**AI 实战交流群:**
...
What ships with it
1 file 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 · 149 lines · 149 tokens per session scan A 37a914141347
wechat-companion is a skill published in the GitHub repository chengkj99/kj-skills (14 stars, last pushed 11d ago), licensed MIT. It adds 149 tokens to every session and 1,656 once invoked, about $0.0007 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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