WeChat Article Publisher
jiji262/wechat-publisher
Researches a topic, writes an illustrated WeChat Official Account article in a chosen author voice and sends it to the account's draft box.
Orchestrates the full WeChat official account workflow from topic to draft, chaining sub-skills for writing, review, layout, images and publishing.
SKILL.md written in Chinese; this summary is our English description.
Install WeChat Official Account Pipeline
$ npx skills add aiworkskills/wechat-article-skills --skill aws-wechat-article-main -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install aiworkskills/wechat-article-skills aws-wechat-article-main --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/aiworkskills/wechat-article-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/aws-wechat-article-main .claude/skills/aws-wechat-article-main && rm -rf skills-srcUse ~/.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/
Install the "aws-wechat-article-main" agent skill from https://github.com/aiworkskills/wechat-article-skills/tree/main/skills/aws-wechat-article-main into .claude/skills/aws-wechat-article-main/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aws-wechat-article-main", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/aiworkskills/wechat-article-skills/tree/main/skills/aws-wechat-article-mainType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add aiworkskills/wechat-article-skills --skill aws-wechat-article-main -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install aiworkskills/wechat-article-skills aws-wechat-article-main --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aiworkskills/wechat-article-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/aws-wechat-article-main .agents/skills/aws-wechat-article-main && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "aws-wechat-article-main" agent skill from https://github.com/aiworkskills/wechat-article-skills/tree/main/skills/aws-wechat-article-main into .agents/skills/aws-wechat-article-main/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aws-wechat-article-main", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add aiworkskills/wechat-article-skills --skill aws-wechat-article-main -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install aiworkskills/wechat-article-skills aws-wechat-article-main --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aiworkskills/wechat-article-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/aws-wechat-article-main .cursor/skills/aws-wechat-article-main && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "aws-wechat-article-main" agent skill from https://github.com/aiworkskills/wechat-article-skills/tree/main/skills/aws-wechat-article-main into .cursor/skills/aws-wechat-article-main/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aws-wechat-article-main", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/aiworkskills/wechat-article-skills.git --path skills/aws-wechat-article-main--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add aiworkskills/wechat-article-skills --skill aws-wechat-article-main -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install aiworkskills/wechat-article-skills aws-wechat-article-main --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aiworkskills/wechat-article-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/aws-wechat-article-main .gemini/skills/aws-wechat-article-main && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "aws-wechat-article-main" agent skill from https://github.com/aiworkskills/wechat-article-skills/tree/main/skills/aws-wechat-article-main into .gemini/skills/aws-wechat-article-main/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aws-wechat-article-main", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install aiworkskills/wechat-article-skills aws-wechat-article-mainInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add aiworkskills/wechat-article-skills --skill aws-wechat-article-main -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/aiworkskills/wechat-article-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/aws-wechat-article-main .github/skills/aws-wechat-article-main && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "aws-wechat-article-main" agent skill from https://github.com/aiworkskills/wechat-article-skills/tree/main/skills/aws-wechat-article-main into .github/skills/aws-wechat-article-main/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aws-wechat-article-main", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add aiworkskills/wechat-article-skills --skill aws-wechat-article-main -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install aiworkskills/wechat-article-skills aws-wechat-article-main --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aiworkskills/wechat-article-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/aws-wechat-article-main .opencode/skills/aws-wechat-article-main && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "aws-wechat-article-main" agent skill from https://github.com/aiworkskills/wechat-article-skills/tree/main/skills/aws-wechat-article-main into .opencode/skills/aws-wechat-article-main/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aws-wechat-article-main", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
aws-wechat-article-main- GitHub stars
- 672
- Token cost
- ~2k tokens
- SKILL.md length
- 416 words
- Files
- 26 (incl. scripts, references)
- Skills in repo
- 9
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Orchestrates the full WeChat official account workflow from topic to draft, chaining sub-skills for writing, review, layout, images and publishing.
- Works in 5 steps: 配置自检 ⛔ → 全局账号约束 ⛔ → 本篇准备 → …
- Writing and publishing a WeChat official account article from scratch
- SKILL.md covers 能力披露（Capabilities）, 配置检查 ⛔ BLOCKING, 主要配置文件（不要混用） and 交互顺序, plus 3 more sections
- Planning a content calendar or series for an official account
What it does
This is the entry point for a suite of skills that take a WeChat official account article from topic selection through writing, review, formatting, cover and inline images, and publishing. The entry skill itself only validates the environment with `scripts/validate_env.py`, checking that the API keys and WeChat account slots in `aws.env` exist and are non-empty. The sub-skills do the real work and make the outbound calls.
Setup is gated: the agent walks through a first-time checklist covering the OS, Python and the config files (`aws.env` for secrets, `.aws-article/config.yaml` for account-level settings, and an `article.yaml` per article), and may not continue if a step fails. Publishing defaults to the draft box and moves to published only when you ask. The agent must ask whether to continue an earlier article or start a new one rather than assume. The suite calls external language-model, image and WeChat APIs and sends API keys and the article content with those calls, so check that before use.
When your agent uses it
- Writing and publishing a WeChat official account article from scratch
- Planning a content calendar or series for an official account
- Resuming a half-finished article from an earlier session
- Finding a topic and headline for today's post
Example prompts
- “Write a WeChat official account article about our new product launch and save it to the draft box.”
- “What should I write about today for my official account?”
- “Pick up where we left off on yesterday's article.”
Requirements
- Python to run `scripts/validate_env.py`
- API keys in `aws.env` for the writing, image and WeChat services in use
- WeChat official account app credentials, unless publishing is turned off
Workflow steps
5 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit ca036f7. 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
Ships 1 file in scripts/, which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
WeChat Official Account Pipeline loads about 2k tokens when it runs, and up to ~14k if it reads all its reference files. Until then it costs about 147 tokens; SKILL.md has 416 words of instructions outside code blocks.
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 passed
The automated check found no risky patterns in SKILL.md.
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); the scripts in this folder are not scanned.
SKILL.md
The full file from aiworkskills/wechat-article-skills at commit ca036f7, republished under its Apache-2.0 licence (© aiworkskills). 416 words, ~2,028 tokens.
Download SKILL.mdSave it as
.claude/skills/aws-wechat-article-main/SKILL.md (or your agent's skills folder). This skill also uses 25 other files; get the full folder from GitHub.- name
- aws-wechat-article-main
- description
- 公众号运营｜微信公众号｜公众号一条龙｜公众号全流程｜自媒体运营｜wechat automation｜content pipeline｜AIGC workflow — 公众号一条龙运营总控入口，选题→写稿→审稿→排版→配图→发布串联 8 个子 skill，单条指令完成整篇图文从 0 到上架。面向公众号小编、自媒体、品牌内容。触发词分层：**一条龙流程**「一条龙」「完整流程」「从头做」「从 0 到发布」；**新做新发**「帮我写篇公众号文章」「做一篇公众号文章」「我想发一篇」「帮我发一篇」「再来一篇」；**选题起点**「今天写什么好」「有什么好写的」「找个话题」「爆款选题」「热点选题」「起个爆款标题」；**策划起点**「内容日历」「系列策划」「专栏规划」「连载」；**流程恢复**「接着上次那篇」「继续昨天的」「继续上次的」「接着之前的进度」；**显式模型新写**「用 GPT 写一篇」「用 DeepSeek 写一篇」「把提纲写成文章」。子 skill（topics/writing/review/formatting/images/publish/sticker/assets）单独触发仅限对**已有产物**的修改场景（如"改标题""润色这段""排版""审稿""加封面""发布"）；新做/策划/多环节串联一律走本入口。
- homepage
- https://aiworkskills.cn
- url
- https://github.com/aiworkskills/wechat-article-skills
公众号运营总览
一键式公众号 AI 内容流水线 —— 从选题到上架 8 个子 skill 串联。
套件说明 ·
aws-wechat-article-*共 9 个 slug：main / topics / writing / review / formatting / images / publish / assets，外加aws-wechat-sticker。跨 skill 的相对引用依赖同一skills/根目录；推荐clawhub sync一次性全装。源码：https://github.com/aiworkskills/wechat-article-skills
Agent 执行：确定本 SKILL.md 所在目录为 {baseDir}。
能力披露（Capabilities）
本 skill 是编排入口，真正调用外部 API 的是子 skill（writing / images / publish / sticker / review）。本入口自身只跑 validate_env.py：
- 凭证：读
aws.env的各*_API_KEY与WECHAT_{N}_*，仅校验键存在且非空，值不用于任何网络请求 - 网络：本入口无外发；子 skill 有外发，见各自的能力披露
- 文件读写：
.aws-article/、本篇article.yaml - shell：仅
{python} {baseDir}/scripts/validate_env.py
整体套件会调用外部 LLM、图像与微信 API，并在调用时外发 API key 与本篇内容。
只装 main 一个时仍可做环境校验；进入流水线会因子 skill 缺失而 file not found。
配置检查 ⛔ BLOCKING
进入「全局账号约束」「本篇准备」及内容流水线之前，必须按 首次引导 完整走一遍「检测顺序」——判断 OS、探测 {python}、检查两份配置文件、跑 validate_env.py、创建预设目录。任一步失败不得继续。
那份文档同时是唯一的失败引导来源与行为约束（禁止自作主张、不得索取密钥）出处，本文件不再复述。
三个要点在这里说一次：
publish_method默认draft（publish.py full只写公众号草稿箱）。用户明确要求「发出去」才改published，或临时用full --publish。- 用户明确不接微信 → 先设
publish_method: none再过校验，之后full会直接跳过。 - 写作 / 图片模型是可选项，缺失时只警告、不阻断。
主要配置文件（不要混用）
| 文件 | 位置 | 作用 |
|---|---|---|
aws.env | 仓库根 | 只放密钥：*_API_KEY、WECHAT_N_APPID / WECHAT_N_APPSECRET（键名见 references/env.example.yaml） |
.aws-article/config.yaml | 仓库内 | 账号级非密钥配置：文风、选题边界、模型端点、微信槽位与 publish_method（模板见 references/config.example.yaml） |
article.yaml | 本篇目录 | 本篇发文元数据与状态：image_source（generated / user）、publish_completed（新建 false，发布闭环后 true） |
字段说明见 articlescreening-schema.md。
交互顺序
上一步完成再进下一步。 每步若缺必要输入（目录、元数据、主题、用户选择、发布意图），先问用户并取得确认；除非用户说明基于某个历史任务继续，那就按中间产物判断从哪个阶段接入。
1) 配置自检 ⛔
按上文完成检测顺序，validate_env.py 退出码 0 且预设目录已建，才能进下一步。
- 退出码 0 + 可选项警告 → 不阻断，直接进下一步，不得要求用户先配写作/图片模型。
- 退出码 1（微信不完整）→ 见 branches.md 第三节。
validate_env.py不检查article_category/target_reader/default_author，那三项在下一步查。
2) 全局账号约束 ⛔
打开 .aws-article/config.yaml，检查 article_category、target_reader、default_author trim 后是否非空。
三项齐全 → 静默通过，进下一步。任一为空 → 见 branches.md 第四节：逐项问用户、取得当轮答复后写回文件，禁止擅自填写或从别处反推。
3) 本篇准备
不了解用户要续写还是新开时，须先问，再进入下面的步骤。禁止默认「最近修改」的目录、未确认就跑写作脚本、或假定沿用上一轮路径。
用户直接给了 drafts/… 路径 → 走 branches.md 第一节「我已有目录」。
涉及用户自身业务时（产品 / 软件 / 服务）：先 ls .aws-article/products/ 看有无相关目录，有就必读根下 *.md 作底稿、优先复用 images/ 里的现成配图；流程中产生的业务介绍类内容，主动引导用户存回 .aws-article/products/{产品名}/。详见 assets skill。
新建一篇（严格子顺序，勿跳步）
写作意图 ⛔ BLOCKING：必须问清本篇要写什么（主题、角度、体裁或目标）。用户只想「帮我出选题」时须明确确认后再按无方向模式处理。 ⛔ 用户未回答本步之前，禁止
web_search、禁止执行 topics 的调研、禁止批量生成选题或标题。 用户已在当次对话说清楚的，口头确认一句即可，不必重复盘问。定题与 slug：确定发文标题（用户从候选中选或自定义），据此生成 slug，目录名
YYYYMMDD-标题slug。建目录与
article.yaml：创建{drafts_root}/YYYYMMDD-标题slug/（drafts_root以config.yaml为准，默认drafts/）。 ⛔ 必须用{baseDir}/../aws-wechat-article-publish/scripts/article_init.py初始化，不要手写。 手写一定会漏字段——实测三篇连着漏掉image_medium，配图媒介的「避开上一篇」因此永远查不到东西。本篇预设单选落盘（必做）：以
config.yaml为来源，按custom_* > default_*结合本篇主题，为下列字段各选单一预设写回article.yaml为单元素列表：default_structure、default_closing_block、default_title_style、default_format_preset、default_format_scheme、default_cover_image_style、default_sticker_style。⛔
default_article_image_style不在此列，保持多元素候选池原样——正文形态是每个图位各选一个，收敛成一个等于整篇配图用同一种形态。实测三篇都被钉成「对比说明」，而实际三张图是实证、流程图、流程图。⛔ 选模版与配色不要只看名字：先跑
format.py --list-themes，按「适合 / 不适合」对本篇题材，再按配色说明选一档。也不要连着几篇选同一套——同一天发的几篇如果模版、配色、封面形态全同，读者一眼看出是套模板。开工前扫一眼最近几篇：bash grep -h "^default_format_preset:" -A1 $(ls -d drafts/*/ | sort -r | head -3 | sed 's#$#article.yaml#') 2>/dev/null续写 / 重入的处理见 branches.md 第五节。
脚本输出一律落盘 ⛔：调本套件任何脚本都把 stdout 与 stderr 一起重定向到本篇目录下的
<环节>.log，不要只看屏幕。bash {python} {baseDir}/../aws-wechat-article-images/scripts/image_create.py generate … \ > drafts/YYYYMMDD-slug/cover-generation.log 2>&1约定文件名：
cover-generation.log、image-generation.log、format.log、publish.log。这些日志是出问题时唯一的凭据，关键信息只在里面——端点忽略比例把封面腰斩、模版从哪个文件加载、配色有没有应用、正文图传成了哪个 URL，屏幕上滚过去就没了。实测靠cover-generation.log里那行「已按 2.35:1 居中裁切: 1024x1024 → 1024x436」才定位到封面为什么难看。
Show full SKILL.md (172 more words)Show less
4) 内容流水线
选题 → 写稿 → 审稿(内容审) → 排版 → 配图 → 审稿(终审)| 步骤 | 子 skill | 读取 | 产出 |
|---|---|---|---|
| 选题 | topics | config.yaml、web_search | topic-card.md research.md |
| 写稿 | writing | topic-card.md、config.yaml | draft.md |
| 内容审 | review | draft.md、config.yaml | review.md → article.md |
| 排版 | formatting | article.md、config.yaml | article.html |
| 配图 | images | article.md、config.yaml | imgs/、cover.* |
| 终审 | review | article.html、imgs/ | review.md |
⛔ 内容审产出的 article.md 定稿须先满足 review 第 5 步（文末 {embed:…}，BLOCKING）再排版。
topics / web_search 须在 3-1（用户已说明写什么，或已确认只帮出选题）之后才可执行。
确认轮次：全局三键非空时静默通过；publish_method 合法时不重复盘问；多个待确认项合并为一轮。用户意图明确时理想轮次为 1 轮（确认标题/摘要）+ 写完展示。配图在用户无特殊要求时按默认风格自动执行，不单独确认。
5) 发布
以 config.yaml 的 publish_method 为准：draft → publish.py full 只写草稿箱；published 或 full --publish → 再提交发布；none → full 无操作退出。
前两档需微信凭证配齐，建议先跑 publish.py check-wechat-env。完成后输出小结与回执，目录按需移至 published_root。
用户说「草稿箱里图不满意要换图重发」→ 见 branches.md 第二节。
中间产物门禁 ⛔
进入下一步前先检查本步所需产物；缺什么补什么，禁止跳步并宣称已完成。
| 阶段 | 必要产物 | 缺失时 |
|---|---|---|
| 写稿完成 | article.md 存在且非空 | 继续写稿或回 writing |
| 排版完成 | article.html 存在，且由当前 article.md 重新生成 | 先重跑 formatting |
| 配图完成 | 有 cover.(png/jpg/jpeg/webp)；article.md 与 article.html 都不含 placeholder | 先跑 images 生成并替换 |
| 发布就绪 | article.yaml 含 title/author/digest/content_source；发布环境检查通过 | 先补元数据或环境 |
| 发布闭环 | 发布命令成功且回执可用 | 才允许写回 publish_completed: true |
禁止仅凭单一信号（如「草稿创建成功」）就宣称全流程完成。正文仍有 placeholder 时，状态必须标记为「草稿已提交，正文配图未完成」。
路由
默认与长文发文相关时优先 main，由本 skill 按步编排；不要因为用户说了「写」「选题」「发」就跳过 main 直连子 skill。
何时可直连子 skill：用户明确只要该步产物且不隐含「从零到发出」整条链。
| 用户说法 | 路由到 |
|---|---|
| 从0开始、一条龙、完整流程、帮我发一篇、发到公众号、今天写什么好（要成文并发）、不确定从哪步开始 | main |
| 「能不能发」且含代为发布、或要从稿到发出整条收尾 | main |
| 只要选题卡 / 标题 / 摘要 / 排期 / 系列策划 | topics |
| 只要在已有选题或草稿上写稿 / 改写 / 润色 / 续写 | writing |
| 只要审稿 / 校对 / 合规清单 | review |
| 只要排版 / 换主题 / 转 HTML | formatting |
| 只要长文封面或正文配图 | images |
| 只要执行发布 / 提交 / 群发 | publish |
| 贴图、图片消息、多图推送、九宫格 | sticker |
表述含糊、可能还要后续发文的，仍从 main 问起。单步子 skill 的边界见 branches.md 第六节。
分支与补充规则
不在主路径上的情况全部写在 references/branches.md：已有目录分支、发布后换图重发、退出码 1 的处理、全局三键为空时怎么问、续写重入的预设处理、单步子 skill 的边界。
© aiworkskills, Apache-2.0. 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 25 other files (scripts, references) in skills/aws-wechat-article-main of aiworkskills/wechat-article-skills.
- SKILL.md
- references/articlescreening-schema.md
- references/branches.md
- references/config.example.yaml
- references/env.example.yaml
- references/first-time-setup.md
- references/presets/closing-blocks/README.md
- references/presets/closing-blocks/simple.example.md
- references/presets/cover-styles/README.md
- references/presets/cover-styles/简约.example.md
- references/presets/image-styles/README.md
- references/presets/image-styles/flat-vector.example.md
- references/presets/review-rules.example.yaml
- references/presets/sticker-styles/README.md
- references/presets/sticker-styles/default.example.md
- … and 11 more
Open the folder on GitHubat commit ca036f7
Compare with similar skills
WeChat Official Account Pipeline 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| WeChat Official Account Pipeline this skillaiworkskills/wechat-article-skills | 672 | — | ~2k | Automated safety check: Pass | Apache-2.0 | |
| WeChat Article Publisherjiji262/wechat-publisher | 274 | — | ~4.4k | Automated safety check: Pass | None | |
| Content Engineericrisco/rsc-harness | 180 | — | ~2.9k | Automated safety check: Pass | MIT | |
| WeChat Hot Article AnalysisSpaceZephyr/creator-buddy | 1.6k | — | ~847 | Automated safety check: Pass | None | |
| Brand Voice Content Creatordavila7/claude-code-templates | 33k | 3 repos | ~1.9k | Automated safety check: Pass | MIT | |
| Web Content Fetcheryaomindong1996/forge-admin | 126 | — | ~898 | Automated safety check: Pass | Apache-2.0 |
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All 9 skills in this repoWeChat Article Assets Library
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Manages a local business-material library and .aws preset packs for a WeChat official-account article workflow, including product images and theme presets.
WeChat Article Formatter
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Converts a Markdown WeChat article draft into themed, paste-ready HTML, with no network calls or credentials and a choice of built-in visual templates.
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Publishes a finished article and its images directly to a WeChat official account through its official API, as a draft or a live broadcast.
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Rewrites, continues, or polishes an existing WeChat article draft using a configurable language model, sending the full draft text to that endpoint.
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Local, offline pre-publish review for a WeChat official account article: sensitive words, typos and platform compliance, producing an actionable fix list.
Related
Questions about WeChat Official Account Pipeline
What does WeChat Official Account Pipeline do?
Orchestrates the full WeChat official account workflow from topic to draft, chaining sub-skills for writing, review, layout, images and publishing. This is the entry point for a suite of skills that take a WeChat official account article from topic selection through writing, review, formatting, cover and inline images, and publishing.env` exist and are non-empty.
When should I use WeChat Official Account Pipeline?
WeChat Official Account Pipeline fits situations like: writing and publishing a WeChat official account article from scratch; planning a content calendar or series for an official account; resuming a half-finished article from an earlier session; finding a topic and headline for today's post.
How do I install WeChat Official Account Pipeline in Claude Code?
Run `npx skills add aiworkskills/wechat-article-skills --skill aws-wechat-article-main -a claude-code`. Or copy the skill folder (skills/aws-wechat-article-main in aiworkskills/wechat-article-skills) into .claude/skills/aws-wechat-article-main in your project. Claude Code loads it when a task matches its description.
How do I install WeChat Official Account Pipeline in Codex?
Run `npx skills add aiworkskills/wechat-article-skills --skill aws-wechat-article-main -a codex`. Or copy the skill folder (skills/aws-wechat-article-main in aiworkskills/wechat-article-skills) into .agents/skills/aws-wechat-article-main in your project. Codex loads it when a task matches its description.
Can I use WeChat Official Account Pipeline 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 aiworkskills/wechat-article-skills --skill aws-wechat-article-main -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/aws-wechat-article-main, .gemini/skills/aws-wechat-article-main, .github/skills/aws-wechat-article-main and .opencode/skills/aws-wechat-article-main in your project.
What does WeChat Official Account Pipeline need to run?
SKILL.md names no scripts, command-line tools or credentials: WeChat Official Account Pipeline is instructions for the agent only. Our summary lists: Python to run `scripts/validate_env.py`; API keys in `aws.env` for the writing, image and WeChat services in use; WeChat official account app credentials, unless publishing is turned off.
Does WeChat Official Account Pipeline access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is WeChat Official Account Pipeline safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does WeChat Official Account Pipeline use?
WeChat Official Account Pipeline is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does WeChat Official Account Pipeline use?
About 2k tokens (SKILL.md is roughly 8.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 12k tokens, read only when the agent opens those files.
What are the alternatives to WeChat Official Account Pipeline?
Skills that share tags, products or a category with WeChat Official Account Pipeline: WeChat Article Publisher (jiji262/wechat-publisher, 274 stars), Content Engine (ericrisco/rsc-harness, 180 stars), WeChat Hot Article Analysis (SpaceZephyr/creator-buddy, 1.6k stars) and Brand Voice Content Creator (davila7/claude-code-templates, 33k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains WeChat Official Account Pipeline?
aiworkskills (a GitHub user) maintains it in aiworkskills/wechat-article-skills, which has 672 GitHub stars. The repository holds 9 skills in this directory. The repository was last updated on September 23, 2026.
Source: aiworkskills/wechat-article-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.