wewrite-wechat-ai-publishing — AI-инструмент для автоматической публикации в WeChat

--- name: wewrite-wechat-ai-publishing description: Full-pipeline AI skill for WeChat Official Account articles — hotspot fetching, topic selection, writing, SEO, image generation, formatting, and draft box publishing. triggers: - "写一篇公众号文章" - "帮我写微信公众号" - "抓取热点写文章" - "发布文章到微信草稿箱" - "生成公众号封面图" - "分析SEO关键词" - "排版微信文章" - "用wewrite写文章" --- # WeWrite — WeChat AI Publishing Skill > Skill by [ara.so](https://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 ```bash # 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 ```bash cd wewrite pip install -r requirements.txt ``` ### Configuration ```bash cp config.example.yaml config.yaml ``` Edit `config.yaml`: ```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: ```bash 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. ```bash python3 scripts/fetch_hotspots.py --limit 20 python3 scripts/fetch_hotspots.py --limit 10 --json # JSON output ``` Output example: ```json [ {"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. ```bash 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: ```python 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 ```bash # 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: ```python 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. ```bash # Preview in browser python3 toolkit/cli.py preview article.md --theme professional-clean # Available themes python3 toolkit/cli.py themes ``` Python usage: ```python 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 ```bash 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: ```python 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) ```bash python3 scripts/fetch_stats.py --article-id "your_article_id" ``` ### 9. Learn from Manual Edits ```bash 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`: ```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: ```bash mkdir clients/my-client cp clients/demo/style.yaml clients/my-client/style.yaml # Edit style.yaml for your client ``` --- ## Themes | Theme | Style | |---|---| | `professional-clean` | Clean professional (default) | | `tech-modern` | Tech-forward blue/purple gradient | | `warm-editorial` | Warm editorial tones | | `minimal` | Minimal black/white | ```bash python3 toolkit/cli.py themes # list all themes with previews python3 toolkit/cli.py preview article.md --theme warm-editorial ``` Custom theme (YAML): ```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 ```python 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: | File | Purpose | |---|---| | `references/topic-selection.md` | 10-topic scoring rules (heat × fit × differentiation) | | `references/frameworks.md` | 5 article structure templates | | `references/writing-guide.md` | Style rules, de-AI-ification techniques | | `references/seo-rules.md` | WeChat SEO: title length, keyword density, tags | | `references/visual-prompts.md` | Image generation prompt templates | | `references/wechat-constraints.md` | WeChat HTML/CSS technical limits | | `references/style-template.md` | Client style.yaml schema documentation | --- ## Troubleshooting **WeChat API 40001 / invalid credential** ```bash # 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** ```bash # 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** ```bash # 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** ```bash # 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 ```bash # 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" ```