获取、解析并保存微信公众号文章，支持单篇和批量下载、元数据提取、图片下载和 Markdown 转换。
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionwechat-article-fetcherExecute the skills CLI command in your project's root directory to begin installation:
Fetches wechat-article-fetcher from wwwzhouhui/skills_collection and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate wechat-article-fetcher. Access via /wechat-article-fetcher in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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获取、解析并保存微信公众号文章，支持单篇和批量下载、元数据提取、图片下载和 Markdown 转换。
获取单篇文章：
python scripts/fetch_wechat_article.py "https://mp.weixin.qq.com/s/xxxxx"
批量获取多篇文章（空格分隔）：
python scripts/fetch_wechat_article.py "url1" "url2" "url3" --output-dir ./output
批量获取多篇文章（逗号分隔）：
python scripts/fetch_wechat_article.py "url1,url2,url3" --output-dir ./output
仅输出元数据（不保存文件）：
python scripts/fetch_wechat_article.py "https://mp.weixin.qq.com/s/xxxxx" --json
pip install beautifulsoup4 html2text requests
python scripts/fetch_wechat_article.py "<url>" --output-dir ./output
输出目录结构：
output/<公众号名称>/<日期>_<标题>/
├── index.html # 格式化的独立HTML文件
├── article.md # Markdown版本
├── meta.json # 文章元数据
└── images/ # 下载的图片
python scripts/fetch_wechat_article.py "<url>" --json
返回 JSON 包含：title（标题）、author（作者）、account_nickname（公众号名称）、description（摘要）、create_time（发布时间）、content_text（正文文本）、content_markdown（Markdown内容）、cover_image（封面图）、source_url（原文链接）。
空格分隔多个链接：
python scripts/fetch_wechat_article.py "url1" "url2" "url3" --output-dir ./output
逗号分隔多个链接：
python scripts/fetch_wechat_article.py "url1,url2,url3" --output-dir ./output
自定义下载间隔（默认3秒，避免触发反爬）：
python scripts/fetch_wechat_article.py "url1" "url2" --interval 5
同一公众号的文章自动归类到同一目录下。
python scripts/fetch_wechat_article.py "<url>" --no-images
python scripts/fetch_wechat_article.py "<url>" --no-images
from scripts.fetch_wechat_article import fetch_article, batch_fetch
# 单篇获取并保存
result = fetch_article("https://mp.weixin.qq.com/s/xxxxx", output_dir="./output")
print(result['title'], result['path'])
# 单篇仅获取元数据
meta = fetch_article("https://mp.weixin.qq.com/s/xxxxx", json_only=True)
print(meta['title'])
print(meta['content_text'][:200])
# 批量获取
urls = ["https://mp.weixin.qq.com/s/aaa", "https://mp.weixin.qq.com/s/bbb"]
stats = batch_fetch(urls, output_dir="./output", interval=3.0)
print(f"成功{stats['success']}篇, 失败{stats['fail']}篇")
主要函数参数：
url：文章链接（支持短链接和长链接）output_dir：保存目录（默认：./wechat_articles）download_img：是否下载图片（默认：True）to_markdown：是否转换为 Markdown（默认：True）json_only：仅返回元数据字典，不保存文件batch_fetch 额外参数：
urls：文章链接列表interval：每篇文章之间的下载间隔秒数（默认：3.0）/s/xxxxx）—— 带 __biz 参数的长链接可能触发验证码。--interval 调整，避免触发微信反爬机制。data-src 属性（非 src），因为采用了懒加载。Referer: https://mp.weixin.qq.com/ 请求头。Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
wechat-article-fetcher has been reliable in day-to-day use. Documentation quality is above average for community skills.
Solid pick for teams standardizing on skills: wechat-article-fetcher is focused, and the summary matches what you get after install.
We added wechat-article-fetcher from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Keeps context tight: wechat-article-fetcher is the kind of skill you can hand to a new teammate without a long onboarding doc.
Registry listing for wechat-article-fetcher matched our evaluation — installs cleanly and behaves as described in the markdown.
wechat-article-fetcher fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
We added wechat-article-fetcher from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Useful defaults in wechat-article-fetcher — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
wechat-article-fetcher has been reliable in day-to-day use. Documentation quality is above average for community skills.
Useful defaults in wechat-article-fetcher — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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