Agent skill

Zhy Wechat Writing

by LeoYeAI in LeoYeAI/openclaw-master-skills

A skill your agent uses when generating a complete WeChat article from a topic, with optional source research, evidence tracking, illustration, HTML conversion, and draft-box publishing.

MITAuto-check: notesProductivity & Automation

Install Zhy Wechat Writing

skills CLI
$ npx skills add LeoYeAI/openclaw-master-skills --skill zhy-wechat-writing -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install LeoYeAI/openclaw-master-skills zhy-wechat-writing --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/zhy-wechat-writing .claude/skills/zhy-wechat-writing && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
zhy-wechat-writing
GitHub stars
2.2k
Token cost
~3.8k tokens
SKILL.md length
634 words
Files
3
Skills in repo
1,235
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when generating a complete WeChat article from a topic, with optional source research, evidence tracking, illustration, HTML conversion, and draft-box publishing.

  • Works in 9 steps: Preflight → 素材搜集 → 初稿生成 → …
  • Generating a complete WeChat article from a topic
  • SKILL.md covers Purpose, When to Use, Prerequisites and Workflow, plus 4 more sections
  • Calls node and npm; reaches mp.weixin.qq.com; needs WECHAT_APP_SECRET

What it does

Zhy Wechat Writing is an agent skill from LeoYeAI/openclaw-master-skills. Use when generating a complete WeChat article from a topic, with optional source research, evidence tracking, illustration, HTML conversion, and draft-box publishing.

Its SKILL.md is about 3.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `README.md` and `_meta.json`).

It sits in Productivity & Automation, covering Messaging and chat bots. It works with WeChat, Reddit, X (Twitter) and GitHub. The repository describes itself as: 🧠 Curated collection of 1209+ best OpenClaw skills — weekly updated by MyClaw.ai. The licence is MIT.

When your agent uses it

  • Generating a complete WeChat article from a topic
  • With optional source research
  • Evidence tracking
  • HTML conversion

Example prompts

  • “/zhy-wechat-writing”

Workflow steps

9 steps, taken from the step headings in SKILL.md.

  1. Preflight
  2. 素材搜集
  3. 初稿生成
  4. 智能审稿
  5. 润色打磨
  6. 保存与输出
  7. 自动配图(可选,调用 zhy-article-illustrator)
  8. HTML 主题样式输出(可选,调用 zhy-markdown2wechat)
  9. 保存到公众号草稿箱(可选,调用 zhy-wechat-publish)

What it can do on your machine

Read from SKILL.md and the folder at commit e5199b5. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • node
    • npm

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • mp.weixin.qq.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • WECHAT_APP_SECRET

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Zhy Wechat Writing loads about 3.8k tokens when it runs. Until then it costs about 46 tokens; SKILL.md has 634 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~46
When it runs · the whole SKILL.md, loaded when a task matches
~3.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NoteMentions a .env fileSKILL.md:453
    - `zhy-wechat-publish` 技能目录下的 `.env` 已配置 `WECHAT_APP_ID` 与 `WECHAT_APP_SECRET`
  • NoteMentions a .env fileSKILL.md:484
    `wechat_draft.js` 未提供 `--thumb` 时会自动读取 `.env` 中的 `WECHAT_DEFAULT_THUMB_MEDIA_ID`
  • NoteMentions a .env fileSKILL.md:503
    3] invalid appid` | AppID 错误,检查发布技能目录下的 `.env` |
  • NoteMentions a .env fileSKILL.md:506
    生图环境未配置,检查 `zhy-article-illustrator` 相关 `.env` |

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its MIT licence (© LeoYeAI). 634 words, ~3,834 tokens.

Download SKILL.mdSave it as .claude/skills/zhy-wechat-writing/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
zhy-wechat-writing
description
Use when generating a complete WeChat article from a topic, with optional source research, evidence tracking, illustration, HTML conversion, and draft-box publishing.
version
3.6.0
author
zhy

微信公众号写作系统

Purpose

根据用户提供的主题(可选参考URL),自动完成公众号文章写作全流程:多来源检索与证据池整理、初稿生成、自审润色、参考资料整理,并可选自动配图与保存到公众号草稿箱(不提交发布)。

When to Use

  • 用户请求"写一篇关于XXX的公众号文章"
  • 用户请求"生成公众号文章,主题是XXX"
  • 用户需要完整的公众号文章创作流程
  • 用户希望"写完后自动配图"或"写完后发到公众号草稿箱"

Prerequisites

执行前需要确认:

  • 用户已提供文章主题(topic)
  • 如有参考文章 URL,可一并提供(urls)
  • 若 topic 为纯中文且未提供 slug,建议补充英文/拼音 kebab-case 目录名;否则会使用 ASCII 降级方案

Workflow

按照以下步骤顺序执行(产物默认落盘到 articles/<slug>/...,便于复跑与追溯):

Phase 0: Preflight

目标:确定可稳定复用的目录与路径规范

操作:

  1. 计算 slug
    • 若用户提供 slug:直接使用(推荐:英文/拼音kebab-case)
    • 若 topic 含拉丁字母/数字:对其做kebab-case
    • 否则降级:wechat-article-YYYYMMDD
  2. 创建目录:
    • articles/<slug>/
    • articles/<slug>/sources/
  3. 规范:Markdown图片引用必须使用相对路径与 / 分隔符
Step 1: 素材搜集

目标:搜集与主题相关的素材,并整理为可追溯的证据池

操作:

  1. 若用户提供 urls:并行使用 webfetch 获取内容,提取要点,并记录URL与可获得的发布日期
  2. 若用户未提供 urls:并行使用 WebSearch 做多来源检索(建议覆盖:官方文档 / X(Twitter) / Reddit / 技术论坛 / 微信公众号 / 工程实践)
    • official/authority:官方文档、标准/规范、权威媒体解读
    • community:X(Twitter)、Reddit、论坛/讨论
    • practice:GitHub issues、工程博客、案例复盘
    • 推荐并行 query 模板(按需组合,尽量加年份/时间范围以强调近期):
      • {topic} official documentation / {topic} release notes 2025 2026
      • {topic} site:x.com / {topic} site:twitter.com
      • {topic} site:reddit.com / {topic} site:reddit.com/r/<subreddit>
      • {topic} site:github.com issues / {topic} site:github.com discussions
      • {topic} site:stackoverflow.com / {topic} site:news.ycombinator.com
      • {topic} site:mp.weixin.qq.com (公众号)
      • {topic} 实战 复盘 踩坑 2025 2026 (中文工程实践)
  3. 合并去重,按可信度分级(high/medium/low),形成证据池,落盘:
    • articles/<slug>/sources/evidence.md

工具映射:

  • 本流程中的“搜索”使用:WebSearch
  • 本流程中的“抓取网页内容”使用:webfetch

关于 WebSearch(实现说明):

  • 优先使用运行环境自带的 WebSearch 工具。
  • 若当前环境没有可用的 WebSearch:用 webfetch 抓取公开搜索结果页(SERP),从结果中提取 URL 列表后再并行 webfetch 正文内容。

证据池条目格式(每条必须包含):

  • title
  • url
  • published_at(可得则写)
  • source_type(official/community/practice)
  • key_takeaways(3-6条要点,尽量可直接改写成正文素材)
  • confidence(high/medium/low)

停止条件(建议):

  • 候选来源 8-12 条,其中 medium/high >= 5 条

常见失败处理(新增):

  • X/Reddit 登录墙或无法抓取正文:
    • 优先选择可公开访问的镜像/引用(二手报道需标注 confidence=low/medium),或改抓同一观点的博客/论坛转载;
    • 若必须引用原帖:只使用 WebSearch 结果摘要 + 其他独立来源佐证,不编造细节。
  • SERP 抓取/解析失败(无 WebSearch 回退路径时):
    • 改用“站内检索”策略:直接用 webfetch 抓官方站点的搜索/博客索引页或 GitHub 搜索页;
    • 仍无法覆盖时:向用户要 3-5 个关键 URL 或指定信息源清单。
  • 去重与可信度:
    • 同一事实至少 2 个独立来源佐证;
    • 官方/一手文档优先标 high;社区讨论若无落地细节或无交叉验证标 low。

输出:sources_path(证据池路径)


Step 2: 初稿生成

目标:基于素材生成公众号文章初稿

写作要求:

  1. 标题:吸引眼球,可使用以下技巧

    • 数字型:5个方法让你...
    • 提问型:为什么...?
    • 对比型:A与B的区别...
    • 悬念型:你不知道的...
  2. 开头(前3-5行):

    • 提出痛点或引发共鸣
    • 设置悬念或提出问题
    • 明确文章价值
  3. 正文结构:

    • 使用小标题分段(## 或 ###)
    • 每段200-300字
    • 包含案例、数据支撑
    • 使用列表增强可读性
  4. 结尾:

    • 总结核心观点
    • 引导互动(点赞、在看、评论)
    • 可添加金句或行动呼吁
  5. 字数要求:1500-2500字

  6. 可追溯性要求(新增硬约束):

    • 关键事实/数据/结论必须能在证据池中找到支撑
    • 文章末尾必须追加 ## 参考资料/来源(5-10条链接,尽量带日期)

输出:Markdown 格式初稿,保存到:articles/<slug>/article.md


Step 3: 智能审稿

目标:从四个维度审稿,发现问题

审稿维度:

维度检查要点权重
逻辑论点清晰、论据充分、推理合理30%
表达语言流畅、无AI痕迹、口语化适度25%
数据数据准确、案例恰当、引用规范25%
结构标题吸引、段落分明、首尾呼应20%

新增硬检查:

  • 可追溯性:关键论断是否能在证据池中找到支撑
  • 标题一致性:标题承诺是否在正文明确兑现

审稿操作:

  1. 逐段检查逻辑连贯性
  2. 标记AI痕迹词汇(如"综上所述"、"不难看出"等)
  3. 验证数据和案例的准确性
  4. 检查结构和节奏

评分标准:

  • 90-100分:优秀,可直接发布
  • 80-89分:良好,小幅优化即可
  • 70-79分:合格,需要修改
  • 70分以下:需要重写

输出:审稿报告(包含问题和修改建议),建议保存到:articles/<slug>/sources/review.md


Step 4: 润色打磨

目标:修复问题,提升文章质量

润色操作:

  1. 去除AI痕迹

    • 替换词汇:
      • 综上所述 → 总的来说
      • 总而言之 → 说到底
      • 由此可见 → 所以说
      • 不难看出 → 我们能发现
      • 众所周知 → 大家都知道
    • 避免过于书面化的表达
  2. 增强口语化

    • 适当添加:其实、说实话、不得不说
    • 使用短句,避免长难句
    • 加入过渡词,增强流畅度
  3. 优化节奏

    • 长短句结合
    • 适当使用感叹句、反问句
    • 控制段落长度(建议不超过5行)
  4. 修复审稿问题

    • 根据审稿报告逐项修复
    • 补充缺失的数据或案例
    • 调整不合理的结构

输出:最终文章

强制要求(新增):

  • 最终文章必须保留或补齐 ## 参考资料/来源
  • 参考资料建议保留 5-10 条,按 official / community / practice 分组更佳

Step 5: 保存与输出

操作:

  1. 创建输出目录(如果不存在):
    • articles/<slug>/
  2. 保存文章:
    • articles/<slug>/article.md
  3. 输出执行摘要:
    • article_path
    • sources_path
    • 字数统计
    • 审稿评分

Step 6: 自动配图(可选,调用 zhy-article-illustrator)

触发条件:with_illustrations=true

目标:为文章生成统一风格的高完成度配图,并产出插图版文章

默认策略:

  • article_path:使用 articles/<slug>/article.md
  • slug:复用当前文章 slug
  • density:illustration_density(默认 balanced)
  • upload:illustration_upload(默认 false)
  • aspect_ratio:illustration_aspect_ratio(默认 16:9)
  • prompt_profile:illustration_prompt_profile(默认 nano-banana)
  • text_language:illustration_text_language(默认 zh-CN)
  • english_terms_whitelist:illustration_english_terms_whitelist(默认空)
  • image_provider:illustration_image_provider(默认 xiaomi)
  • image_model:illustration_image_model(默认 gemini-3.1-flash-image-preview)
  • image_size:illustration_image_size(默认 1K)
  • image_base_url:illustration_image_base_url(默认 Xiaomi 接口地址,也支持 Gemini 原生代理)

执行方式:

  1. 优先调用 zhy-article-illustrator 的一键流程脚本:
    bash
    node <zhy-article-illustrator>/scripts/illustrate-article.ts \
      --article articles/<slug>/article.md \
      --slug <slug> \
      --density <illustration_density> \
      --aspect-ratio <illustration_aspect_ratio> \
      --prompt-profile <illustration_prompt_profile> \
      --text-language <illustration_text_language> \
      --image-provider <illustration_image_provider> \
      --image-model <illustration_image_model> \
      [--image-size <illustration_image_size>] \
      [--image-base-url <illustration_image_base_url>] \
      [--upload]
  2. 若 illustration_english_terms_whitelist 非空,则为每个术语追加 --term <value>,例如:
    bash
    --term Playwright --term Chromium --term Firefox --term WebKit
  3. 默认沿用新版配图策略:
    • 先生成文章级 visual-bible.md
    • 再生成 outline.md 与 prompts/
    • 默认图片内文字为简体中文,仅白名单术语保留英文
    • 同一篇文章内所有图片共享统一风格体系
  4. 写作技能在集成时应遵循以下字段映射:
    • article_path -> --article
    • slug -> --slug
    • illustration_density -> --density
    • illustration_aspect_ratio -> --aspect-ratio
    • illustration_prompt_profile -> --prompt-profile
    • illustration_text_language -> --text-language
    • illustration_image_provider -> --image-provider
    • illustration_image_model -> --image-model
    • illustration_image_size -> --image-size
    • illustration_image_base_url -> --image-base-url
    • illustration_upload=true -> --upload

输出:

  • illustrated_article_path: articles/<slug>/article.illustrated.md
  • illustrations_dir: articles/<slug>/illustrations/<slug>/
  • articles/<slug>/illustrations/<slug>/visual-bible.md
  • articles/<slug>/illustrations/<slug>/outline.md
  • articles/<slug>/illustrations/<slug>/prompts/

失败处理:单张失败可重试一次;仍失败则记录并继续,最终输出失败清单。若部分图片失败,也应保留 article.illustrated.md,并插入图片占位注释。


Show full SKILL.md (272 more words)Show less
Step 7: HTML 主题样式输出(可选,调用 zhy-markdown2wechat)

触发条件:with_html_theme=true

目标:使用 zhy-markdown2wechat 技能将 Markdown 转换为带微信内联样式的 HTML

操作:

  1. 选择输入文件:
    • 若 with_illustrations=true 且 articles/<slug>/article.illustrated.md 存在,则使用该文件
    • 否则使用 articles/<slug>/article.md
  2. 将选中的 Markdown 文件记为 <input_markdown>
  3. 调用 zhy-markdown2wechat 技能,执行转换脚本:
    bash
    node <zhy-markdown2wechat>/scripts/convert.js \
     <input_markdown> \
     <zhy-markdown2wechat>/resources/themes/default.css \
     articles/<slug>/article.zhy.html
    • 脚本零依赖(纯 Node.js),无需 npm install,自动在临时目录处理后清理
    • 输出包含 <section id="MdWechat"> 容器与完整内联 CSS 样式
    • 如需换肤,可将第二个参数替换为 resources/themes/ 下的其他主题文件(apple.css / blue.css / dark.css / green.css / notion.css / vibrant.css)
    • <zhy-markdown2wechat> 表示当前环境中该技能的安装目录,运行时应以实际路径为准
  4. 输入文件示例:
    • 若存在插图版文章:<input_markdown>=articles/<slug>/article.illustrated.md
    • 若不存在插图版文章:<input_markdown>=articles/<slug>/article.md

输出:html_article_path(articles/<slug>/article.zhy.html)

失败处理:记录错误并在执行摘要中注明原因,跳过该步骤并继续后续流程


Step 8: 保存到公众号草稿箱(可选,调用 zhy-wechat-publish)

触发条件:post_to_wechat=true

默认行为:通过微信官方 API 保存到草稿箱,不做最终发布提交

前置条件:

  • zhy-wechat-publish 技能目录下的 .env 已配置 WECHAT_APP_ID 与 WECHAT_APP_SECRET
  • 运行机器的公网 IP 已加入微信公众号后台 IP 白名单
  • 若要自动生成封面,发布技能依赖的生图环境也必须可用(由 zhy-article-illustrator 提供)

调用方式:

  • 正文必须是带内联样式的 HTML 文件
  • 优先使用 Step 7 生成的 article.zhy.html;若不存在则跳过本步骤(或先补执行 Step 7)
  • 默认推荐的稳定入口是直接调用 wechat_draft.js:
    bash
    node <zhy-wechat-publish>/scripts/wechat_draft.js \
      --title "文章标题" \
      --file "articles/<slug>/article.zhy.html" \
      [--author "作者"] \
      [--digest "摘要"] \
      [--thumb "封面media_id"] \
      [--source-url "原文链接"] \
      [--need-open-comment "1"] \
      [--only-fans-can-comment "1"]
  • 若希望自动生成封面并发布,也可调用:
    bash
    node <zhy-wechat-publish>/scripts/publish_with_cover.js \
      --article "articles/<slug>/article.md" \
      --html "articles/<slug>/article.zhy.html" \
      [--title "文章标题"] \
      [--author "作者"] \
      [--source-url "原文链接"] \
      [--need-open-comment "1"] \
      [--only-fans-can-comment "1"]
  • <zhy-wechat-publish> 表示当前环境中该技能的安装目录,运行时应以实际路径为准
  • wechat_draft.js 未提供 --thumb 时会自动读取 .env 中的 WECHAT_DEFAULT_THUMB_MEDIA_ID
  • publish_with_cover.js 会自动从文章中提取标题/摘要、生成单张 16:9 封面、上传封面,并将返回的 media_id 作为 thumb_media_id
  • 发布脚本会在上传前自动展开 HTML 中的 var(--xxx) 样式变量,避免微信草稿箱丢失颜色与边框样式
  • 发布脚本会在上传前自动将正文中的图片上传到微信正文图片接口,并将 <img src> 替换为微信返回的图片 URL
  • 发布脚本会在上传前将原生列表结构降级为“普通段落 + 圆点/编号”,以兼容微信草稿箱再次进入编辑模式时的列表解析问题

注意:

  • 脚本零依赖(纯 Node.js >= 16),无需 npm install
  • 使用 publish_with_cover.js 时,需要本机可用 bun,因为封面生成会复用现有生图脚本
  • 草稿保存后不会自动提交发布,需人工在公众号后台确认
  • 标题长度不得超过 64 字符
  • 若当前环境没有可用生图配置,优先改用 wechat_draft.js 直接上传 HTML,避免自动封面步骤失败

成功标准:输出 上传草稿成功! 草稿 MEDIA_ID: xxx

失败排障清单:

错误信息原因与处理
[40013] invalid appidAppID 错误,检查发布技能目录下的 .env
[40164] invalid ip当前 IP 未加白名单,将报错中的 IP 加入公众号后台
[40007] invalid media_id封面图 ID 无效,使用 upload_image.js 重新上传获取
缺少 Xiaomi/Gemini/OpenAI API Key自动封面生成依赖的生图环境未配置,检查 zhy-article-illustrator 相关 .env
article.zhy.html 不存在Step 7 未执行或失败,检查 with_html_theme=true
标题过长控制标题 <= 64 字符

Data Flow

用户输入(topic, urls?, slug?, search_count?, time_range_days?, ...)
         ↓
Preflight(确定slug与目录)
         ↓
素材搜集(WebSearch + webfetch → evidence.md)
         ↓
初稿生成(article.md)
         ↓
智能审稿(含可追溯性/标题一致性)
         ↓
润色打磨(强制References)
         ↓
自动配图(article.illustrated.md + illustrations/)
         ↓
HTML 主题样式输出(zhy-markdown2wechat → article.zhy.html)
         ↓
保存到草稿箱(不提交发布)

Error Handling

异常情况处理方式
搜索无结果提示用户提供更多信息或参考URL
参考文章无法访问跳过该URL,继续处理其他素材
初稿质量过低重新生成或提示用户提供更多素材
审稿评分<70建议用户检查主题是否合适
配图失败输出失败清单;可选择补图后再发布
HTML 转换失败记录错误并跳过该步骤(Step 7),继续后续流程
发布到草稿箱失败输出排障清单(AppID/IP白名单/封面media_id/标题长度)

Example Usage

输入:

topic: "如何提高工作效率"
urls: ["https://mp.weixin.qq.com/xxx"]
search_count: 5
with_illustrations: true
with_html_theme: true
post_to_wechat: true

执行流程:

  1. 搜索"如何提高工作效率"相关文章
  2. 获取用户提供的参考文章内容
  3. 生成初稿(约1500-2500字)
  4. 审稿评分:85分
  5. 润色优化后保存

输出:

article_path: articles/how-to-improve-work-efficiency/article.md
sources_path: articles/how-to-improve-work-efficiency/sources/evidence.md
illustrated_article_path: articles/how-to-improve-work-efficiency/article.illustrated.md
illustrations_dir: articles/how-to-improve-work-efficiency/illustrations/how-to-improve-work-efficiency/
html_article_path: articles/how-to-improve-work-efficiency/article.zhy.html
word_count: 2150
review_score: 92
wechat_draft_status: success

Notes

  • 文章风格应符合公众号调性:轻松、有用、有共鸣
  • 避免敏感内容和过度营销
  • 保持原创性,不要直接复制素材内容

© LeoYeAI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files in skills/zhy-wechat-writing of LeoYeAI/openclaw-master-skills.

  • SKILL.md
  • README.md
  • _meta.json

Open the folder on GitHubat commit e5199b5

Compare with similar skills

Zhy Wechat Writing next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Zhy Wechat Writing compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Zhy Wechat Writing this skillLeoYeAI/openclaw-master-skills2.2k—~3.8kAutomated safety check: NotesMIT
Content CollectorvigorX777/content-collector-skill240—~2kAutomated safety check: PassNone
Agent ReachEdisonChenAI/agent-reach1151 repos~1.3kAutomated safety check: PassMIT
Feedgrab BatchiBigQiang/feedgrab614—~1.8kAutomated safety check: PassMIT
Media WritershareAI-lab/lab-skills315—~870Automated safety check: PassApache-2.0
FeedgrabiBigQiang/feedgrab614—~2kAutomated safety check: PassMIT

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Questions about Zhy Wechat Writing

What does Zhy Wechat Writing do?

A skill your agent uses when generating a complete WeChat article from a topic, with optional source research, evidence tracking, illustration, HTML conversion, and draft-box publishing. Zhy Wechat Writing is an agent skill from LeoYeAI/openclaw-master-skills. Use when generating a complete WeChat article from a topic, with optional source research, evidence tracking, illustration, HTML conversion, and draft-box publishing.

When should I use Zhy Wechat Writing?

Zhy Wechat Writing fits situations like: generating a complete WeChat article from a topic; with optional source research; evidence tracking; HTML conversion.

How do I install Zhy Wechat Writing in Claude Code?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill zhy-wechat-writing -a claude-code`. Or copy the skill folder (skills/zhy-wechat-writing in LeoYeAI/openclaw-master-skills) into .claude/skills/zhy-wechat-writing in your project. Claude Code loads it when a task matches its description.

How do I install Zhy Wechat Writing in Codex?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill zhy-wechat-writing -a codex`. Or copy the skill folder (skills/zhy-wechat-writing in LeoYeAI/openclaw-master-skills) into .agents/skills/zhy-wechat-writing in your project. Codex loads it when a task matches its description.

Can I use Zhy Wechat Writing in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add LeoYeAI/openclaw-master-skills --skill zhy-wechat-writing -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/zhy-wechat-writing, .gemini/skills/zhy-wechat-writing, .github/skills/zhy-wechat-writing and .opencode/skills/zhy-wechat-writing in your project.

What does Zhy Wechat Writing need to run?

Going by SKILL.md and its folder, Zhy Wechat Writing needs the command-line tools its instructions call (node and npm) and credentials named WECHAT_APP_SECRET.

Does Zhy Wechat Writing access the network?

SKILL.md names 1 domain. In commands or code: mp.weixin.qq.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Zhy Wechat Writing safe to install?

Our automated static check of SKILL.md found notes only (mentions a .env file), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Zhy Wechat Writing use?

Zhy Wechat Writing is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Zhy Wechat Writing use?

About 3.8k tokens (SKILL.md is roughly 15k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Zhy Wechat Writing?

Skills that share tags, products or a category with Zhy Wechat Writing: Content Collector (vigorX777/content-collector-skill, 240 stars), Agent Reach (EdisonChenAI/agent-reach, 115 stars), Feedgrab Batch (iBigQiang/feedgrab, 614 stars) and Media Writer (shareAI-lab/lab-skills, 315 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Zhy Wechat Writing?

LeoYeAI (a GitHub user) maintains it in LeoYeAI/openclaw-master-skills, which has 2,161 GitHub stars. The repository holds 1,235 skills in this directory. The repository was last updated on July 20, 2026.

Source: LeoYeAI/openclaw-master-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.