Token导航 LogoToken导航

Agent Skill · 研究检索

wewrite-wechat-ai-publishing(wewrite 微信 AI 发布)

wewrite-wechat-ai-publishing 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

wewrite-wechat-ai-publishing 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为接触敏感数据;许可证信息还不明确,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 9,450。

总安装 14,223周安装 581GitHub Stars 39下载 4,555核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:wewrite-wechat-ai-publishing(wewrite 微信 AI 发布)
来源仓库:https://github.com/aradotso/trending-skills
仓库路径:skills/wewrite-wechat-ai-publishing
安装命令:
npx skills add https://github.com/aradotso/trending-skills --skill wewrite-wechat-ai-publishing
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/aradotso/trending-skills --skill wewrite-wechat-ai-publishing

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

wewrite 微信 AI 发布用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中快速定位候选结果。它支持基于关键词或任务场景进行信息检索,适用于内容发布场景。通过 npx skills add 命令从指定仓库安装,需结合原始 README 确认具体用法。

使用前应核实权限范围、维护状态,避免触发联网或文件读写操作。wewrite-wechat-ai-publishing 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/wewrite-wechat-ai-publishing

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/aradotso/trending-skills --skill wewrite-wecha…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

wewrite-wechat-ai-publishing 怎么安装?

在终端执行 npx skills add https://github.com/aradotso/trending-skills --skill wewrite-wechat-ai-publi…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

wewrite-wechat-ai-publishing 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

wewrite-wechat-ai-publishing 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

wewrite-wechat-ai-publishing 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

wewrite-wechat-ai-publishing 用的人多吗?

公开数据是累计安装 14,223 次,近一周安装 581 次,下载 4,555 次,GitHub 39 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

wewrite-wechat-ai-publishing 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
wewrite-wechat-ai-publishing

wewrite 微信 AI 发布 · 本页

14,22339接触敏感数据未明确有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

WeWrite — WeChat AI Publishing Skill

Skill by ara.so — Daily 2026 Skills collection.

WeWrite is a full-pipeline AI skill for producing WeChat Official Account (公众号) articles end-to-end: hotspot fetching → topic selection → writing → SEO → AI image generation → formatting → draft box publishing. It runs as a Claude Code skill (via SKILL.md) but every component also works standalone.


Installation

As a Claude Code Skill

# Clone the repo
git clone https://github.com/oaker-io/wewrite.git ~/.claude/skills/wewrite

# Or copy into an existing project
cp -r wewrite ~/.claude/skills/wewrite

Python Dependencies

cd wewrite
pip install -r requirements.txt

Configuration

cp config.example.yaml config.yaml

Edit config.yaml:

wechat:
  appid: "${WECHAT_APPID}"         # WeChat Official Account App ID
  secret: "${WECHAT_SECRET}"       # WeChat Official Account Secret

image_gen:
  provider: "doubao"               # "doubao" or "openai"
  doubao_api_key: "${DOUBAO_API_KEY}"
  openai_api_key: "${OPENAI_API_KEY}"

output_dir: "./output"

Set environment variables instead of hardcoding secrets:

export WECHAT_APPID="wx1234567890abcdef"
export WECHAT_SECRET="your_secret_here"
export DOUBAO_API_KEY="your_doubao_key"
export OPENAI_API_KEY="sk-..."

Triggering the Full Pipeline

Once installed as a Claude Code skill, simply say:

用 demo 的配置写一篇公众号文章

Claude will execute all steps automatically using clients/demo/style.yaml as the client profile.

You can also specify a client:

用 clients/tech-blog 的风格,围绕今日热点写一篇公众号文章,发布到草稿箱

Pipeline Steps & Scripts

1. Fetch Hotspots

Scrapes real-time trending topics from Weibo, Toutiao, and Baidu.

python3 scripts/fetch_hotspots.py --limit 20
python3 scripts/fetch_hotspots.py --limit 10 --json   # JSON output

Output example:

[
  {"rank": 1, "title": "DeepSeek R2 发布", "source": "weibo", "heat": 98200},
  {"rank": 2, "title": "A股科技板块大涨", "source": "baidu", "heat": 75300}
]

2. SEO Keyword Analysis

Queries Baidu and 360 search suggestions to score keywords.

python3 scripts/seo_keywords.py "AI大模型" "科技股"
python3 scripts/seo_keywords.py --json "ChatGPT" "人工智能"

Python usage:

from scripts.seo_keywords import analyze_keywords

results = analyze_keywords(["AI大模型", "大语言模型", "GPT-5"])
for kw in results:
    print(f"{kw['keyword']}: score={kw['score']}, volume={kw['estimated_volume']}")

3. Topic Selection

Claude reads references/topic-selection.md and generates 10 candidate topics scored on:

  • 热度 (trending heat)
  • 契合度 (client fit)
  • 差异化 (differentiation)

4. Framework & Writing

Claude reads references/frameworks.md (5 frameworks) and references/writing-guide.md (de-AI style rules), then writes the article adapted to the client's tone.

5. AI Image Generation

# Cover image (recommended 900×383)
python3 toolkit/image_gen.py \
  --prompt "科技感封面,蓝色光线,未来感" \
  --output output/cover.png \
  --size cover

# Inline content image
python3 toolkit/image_gen.py \
  --prompt "程序员在办公室工作,现代风格插画" \
  --output output/img1.png \
  --provider openai

Python usage:

from toolkit.image_gen import generate_image

path = generate_image(
    prompt="AI机器人与人类握手,科技感插画",
    output_path="output/cover.png",
    size="cover",          # "cover" (900x383) or "content" (800x600)
    provider="doubao"      # "doubao" or "openai"
)
print(f"Generated: {path}")

6. Formatting — Markdown → WeChat HTML

WeChat requires inline styles. The converter handles this automatically.

# Preview in browser
python3 toolkit/cli.py preview article.md --theme professional-clean

# Available themes
python3 toolkit/cli.py themes

Python usage:

from toolkit.converter import MarkdownConverter
from toolkit.theme import load_theme

theme = load_theme("tech-modern")
converter = MarkdownConverter(theme=theme)

with open("article.md") as f:
    markdown_content = f.read()

html = converter.convert(markdown_content)
# html is WeChat-ready with all inline styles

7. Publish to WeChat Draft Box

python3 toolkit/cli.py publish article.md \
  --cover output/cover.png \
  --title "2026年AI大模型最新进展" \
  --author "科技观察"

Python usage:

from toolkit.publisher import WeChatPublisher
from toolkit.wechat_api import WeChatAPI

api = WeChatAPI(appid=os.environ["WECHAT_APPID"], secret=os.environ["WECHAT_SECRET"])
publisher = WeChatPublisher(api=api)

# Upload cover image first
media_id = api.upload_image("output/cover.png")

# Push to draft box
draft_id = publisher.create_draft(
    title="2026年AI大模型最新进展",
    content=html_content,          # inline-styled HTML from converter
    cover_media_id=media_id,
    author="科技观察",
    digest="本文盘点2026年大模型最新进展..."   # summary/excerpt
)
print(f"Draft created: {draft_id}")

8. Fetch Article Stats (回填数据)

python3 scripts/fetch_stats.py --article-id "your_article_id"

9. Learn from Manual Edits

python3 scripts/learn_edits.py \
  --original output/draft.md \
  --edited output/final.md \
  --client demo

Extracts style rules from diffs and appends them to the client's playbook.


Client Configuration

Each client lives in clients/{name}/style.yaml:

# clients/my-tech-blog/style.yaml
name: "我的科技博客"
industry: "科技/AI"
topics:
  - "人工智能"
  - "大模型应用"
  - "编程技术"
tone: "专业严谨,偶尔幽默,面向中级开发者"
theme: "tech-modern"
avoid:
  - "过度营销语言"
  - "绝对化表述"
wechat:
  appid: "${WECHAT_APPID}"
  secret: "${WECHAT_SECRET}"

Create a new client:

mkdir clients/my-client
cp clients/demo/style.yaml clients/my-client/style.yaml
# Edit style.yaml for your client

Themes

ThemeStyle
professional-cleanClean professional (default)
tech-modernTech-forward blue/purple gradient
warm-editorialWarm editorial tones
minimalMinimal black/white
python3 toolkit/cli.py themes          # list all themes with previews
python3 toolkit/cli.py preview article.md --theme warm-editorial

Custom theme (YAML):

# toolkit/themes/my-theme.yaml
name: my-theme
body:
  font-family: "'PingFang SC', sans-serif"
  font-size: "16px"
  color: "#333"
  line-height: "1.8"
h2:
  color: "#1a73e8"
  font-weight: "bold"
  border-left: "4px solid #1a73e8"
  padding-left: "10px"
blockquote:
  background: "#f0f4ff"
  border-left: "3px solid #4285f4"
  padding: "12px 16px"
  color: "#555"

Full Pipeline — Python Orchestration

import subprocess
import json
import os
from toolkit.converter import MarkdownConverter
from toolkit.theme import load_theme
from toolkit.publisher import WeChatPublisher
from toolkit.wechat_api import WeChatAPI
from toolkit.image_gen import generate_image

# 1. Fetch hotspots
result = subprocess.run(
    ["python3", "scripts/fetch_hotspots.py", "--limit", "20", "--json"],
    capture_output=True, text=True
)
hotspots = json.loads(result.stdout)

# 2. SEO analysis on top topics
from scripts.seo_keywords import analyze_keywords
top_titles = [h["title"] for h in hotspots[:5]]
seo_scores = analyze_keywords(top_titles)

# 3. (Claude selects topic, writes article — handled by SKILL.md)
# After Claude produces article.md:

# 4. Generate cover
cover_path = generate_image(
    prompt="科技感封面图,蓝色渐变,数字化未来",
    output_path="output/cover.png",
    size="cover",
    provider=os.environ.get("IMAGE_PROVIDER", "doubao")
)

# 5. Convert Markdown → WeChat HTML
theme = load_theme("tech-modern")
converter = MarkdownConverter(theme=theme)
with open("output/article.md") as f:
    html = converter.convert(f.read())

# 6. Publish to draft box
api = WeChatAPI(
    appid=os.environ["WECHAT_APPID"],
    secret=os.environ["WECHAT_SECRET"]
)
publisher = WeChatPublisher(api=api)
media_id = api.upload_image(cover_path)
draft_id = publisher.create_draft(
    title="选定标题",
    content=html,
    cover_media_id=media_id,
    author="作者名"
)
print(f"✅ Published draft: {draft_id}")

References Claude Uses During Pipeline

These files are read automatically by Claude when executing the skill:

FilePurpose
references/topic-selection.md10-topic scoring rules (heat × fit × differentiation)
references/frameworks.md5 article structure templates
references/writing-guide.mdStyle rules, de-AI-ification techniques
references/seo-rules.mdWeChat SEO: title length, keyword density, tags
references/visual-prompts.mdImage generation prompt templates
references/wechat-constraints.mdWeChat HTML/CSS technical limits
references/style-template.mdClient style.yaml schema documentation

Troubleshooting

WeChat API 40001 / invalid credential

# Access token expires every 2 hours — the API wrapper auto-refreshes,
# but verify your appid/secret are correct:
python3 -c "
from toolkit.wechat_api import WeChatAPI
import os
api = WeChatAPI(os.environ['WECHAT_APPID'], os.environ['WECHAT_SECRET'])
print(api.get_access_token())
"

Image generation fails

# Test provider connectivity
python3 toolkit/image_gen.py \
  --prompt "测试图片" \
  --output /tmp/test.png \
  --provider doubao
# If doubao fails, switch to openai in config.yaml

Markdown conversion missing styles

# Verify theme loads correctly
python3 -c "from toolkit.theme import load_theme; print(load_theme('tech-modern'))"

# Preview output before publishing
python3 toolkit/cli.py preview article.md --theme tech-modern
# Opens browser with rendered HTML

Hotspot fetch returns empty

# Platforms occasionally change their APIs — run with verbose:
python3 scripts/fetch_hotspots.py --limit 5 --verbose
# Check which sources are failing; the script supports weibo/baidu/toutiao independently

Article not appearing in draft box

  • Ensure the WeChat account has 服务号 or 订阅号 API access enabled
  • Check that your IP is whitelisted in the WeChat MP platform settings
  • Draft box (草稿箱) requires draft.add API permission

Quick Reference

# Full standalone workflow
python3 scripts/fetch_hotspots.py --limit 20 --json > hotspots.json
python3 scripts/seo_keywords.py --json "关键词1" "关键词2"
python3 toolkit/image_gen.py --prompt "封面描述" --output cover.png --size cover
python3 toolkit/cli.py preview article.md --theme tech-modern
python3 toolkit/cli.py publish article.md --cover cover.png --title "标题"

# Build playbook from historical articles
python3 scripts/build_playbook.py --client demo

# Learn from edits
python3 scripts/learn_edits.py --original draft.md --edited final.md --client demo

# Fetch article performance data
python3 scripts/fetch_stats.py --article-id "msgid_here"

平台分布

Codex

35.85%
按下载量换算1,633

Claude

27.9%
按下载量换算1,271

Cursor

18.25%
按下载量换算831

Gemini CLI

8.25%
按下载量换算376

安全审计

Gen Agent Trust Hub

通过

Socket

可疑

Snyk

可疑

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

继续浏览同类 Skills