awesome-openclaw-usecases-zh

# awesome-openclaw-usecases-zh > Skill by [ara.so](https://ara.so) — Hermes Skills collection. A comprehensive Chinese-language reference for **OpenClaw** (formerly ClawdBot/MoltBot) use cases, featuring 50+ verified real-world scenarios for AI agent automation. This skill equips AI coding agents with knowledge of OpenClaw patterns, Chinese platform integrations, and production-ready implementations. ## What This Project Provides **awesome-openclaw-usecases-zh** is a curated collection of OpenClaw use cases designed for Chinese users, including: - **23 China-specific use cases**: Feishu, DingTalk, WeChat Work, Xiaohongshu, A-share stock monitoring - **27 international use cases** (many with Chinese adaptations): social media, DevOps, productivity, research - **Structured format**: Each use case includes pain points, capabilities, required skills, setup steps, and practical tips - **Agent-readable structure**: Standardized markdown format suitable for AI consumption ## Core Concepts | Concept | English | Description | |---------|---------|-------------| | 工作区 | Workspace | Agent's working directory | | 灵魂 | SOUL.md | Defines agent personality and boundaries | | 操作手册 | AGENTS.md | Agent's operational instructions | | 记忆 | Memory | Persistent context and preferences | | 技能 | Skill | Reusable knowledge packages | | 工具 | Tool | Specific capabilities (file ops, search, messaging) | | 频道 | Channel | Platform connectors (Telegram, Feishu, Discord) | | 提示词 | Prompt | User instructions to agent | | 定时任务 | Cron Job | Scheduled automation | | 心跳 | Heartbeat | Periodic status checks and reports | | 子智能体 | Sub-agent | Parallel agent spawning | ## Installation & Access The repository is hosted on GitHub and AtomGit (China mirror): ```bash # Clone from GitHub git clone https://github.com/AlexAnys/awesome-openclaw-usecases-zh.git # Clone from AtomGit (China) git clone https://atomgit.com/alex_anys/awesome-openclaw-usecases-zh.git ``` ## Repository Structure ``` awesome-openclaw-usecases-zh/ ├── README.md # Main index with 50+ use cases ├── CONTRIBUTING.md # Contribution guidelines ├── AGENT-GUIDE.md # Guide for AI agents to use this repo ├── usecases/ │ ├── cn-*.md # China-specific use cases (23) │ ├── *.md # International use cases (27) │ └── images/ # Screenshots and diagrams └── templates/ └── usecase-template.md # Standard use case format ``` ## Use Case Categories ### 🇨🇳 China-Specific (23 cases) **Platform Bots (4)** - `cn-feishu-ai-assistant.md` - Feishu/Lark bot integration - `cn-feishu-lark-cli.md` - Lark CLI for agent operations (200+ commands) - `cn-dingtalk-ai-assistant.md` - DingTalk bot (Stream mode) - `cn-wecom-ai-assistant.md` - WeChat Work bot **Content Creation (3)** - `cn-xiaohongshu-automation.md` - Xiaohongshu publishing pipeline - `cn-wechat-mp-automation.md` - WeChat Official Account automation - `podcast-production-pipeline.md` - Podcast workflow (Ximalaya/Bilibili) **Data & Research (7)** - `cn-a-share-monitor.md` - A-share stock monitoring (AKShare) - `earnings-tracker.md` - Earnings reports (Chinese stocks) - `competitive-intelligence.md` - Competitor analysis (Baidu Index, WeChat Index) - `cn-internet-research-30days.md` - 8 Chinese platform aggregation - `hf-papers-research-discovery.md` - HuggingFace papers (Chinese mirrors) - `arxiv-paper-reader-latex-writer.md` - arXiv + LaTeX (Chinese templates) **Office & Customer Service (4)** - `cn-office-automation.md` - Email, files, meeting notes (163/QQ/Outlook) - `meeting-notes-action-items.md` - Meeting transcription (Feishu/Tencent/DingTalk) - `multi-channel-customer-service.md` - Multi-channel support - `cn-ecommerce-multi-agent.md` - E-commerce multi-agent architecture **Personal Assistant (5)** - `custom-morning-brief.md` - Daily briefing (Chinese news sources) - `digital-persona-distillation.md` - Personality extraction (12+ platforms) - `cn-multi-agent-operating-system.md` - Multi-agent OS architecture - `agent-swarm-dev-team.md` - Agent swarm development team - `multica-managed-agents.md` - Agent dashboard (web UI) ### 🌐 International (27 cases) **Social Media (4)** - Reddit, YouTube, X aggregation **Creative & Building (3)** - Content pipelines, product building **Infrastructure & DevOps (5)** - Server self-healing, observability, workflow orchestration **Productivity (16)** - Email, calendar, notes, CRM, personal assistant **Research & Learning (9)** - Knowledge bases, market research, competitive analysis **Finance & Trading (1)** - Prediction market simulation ## Reading Use Cases Each use case follows this structure: ```markdown --- difficulty: ⭐ (copy-paste) | ⭐⭐ (config needed) | ⭐⭐⭐ (technical) platform: [feishu|dingtalk|wecom|xiaohongshu|...] tags: [automation, content-creation, ...] --- # Use Case Title ## 痛点 (Pain Points) What problem this solves ## 它能做什么 (Capabilities) - Feature 1 - Feature 2 ## 所需技能 (Required Skills) - Skill package 1 - Skill package 2 ## 如何设置 (Setup) Step-by-step configuration with copy-paste prompts ## 实用建议 (Practical Tips) Best practices and pitfalls ``` ## Key Patterns for AI Agents ### 1. Chinese Platform Integration **Feishu Bot Example** (`cn-feishu-ai-assistant.md`): ```javascript // Install official Feishu SDK npm install @larksuiteoapi/node-sdk // Initialize bot const lark = require('@larksuiteoapi/node-sdk'); const client = new lark.Client({ appId: process.env.FEISHU_APP_ID, appSecret: process.env.FEISHU_APP_SECRET, }); // Handle incoming messages app.post('/webhook', async (req, res) => { const { event } = req.body; if (event.type === 'message') { const { message_id, content } = event.message; const userInput = JSON.parse(content).text; // Send to OpenClaw agent const response = await openclawAgent.process(userInput); // Reply in Feishu await client.im.message.reply({ message_id, content: JSON.stringify({ text: response }), msg_type: 'text', }); } res.json({ ok: true }); }); ``` **DingTalk Stream Mode** (`cn-dingtalk-ai-assistant.md`): ```python # No public IP needed - uses WebSocket from dingtalk_stream import AckMessage import dingtalk_stream def message_handler(dingtalk_client, message): content = message.text.content.strip() # Process with OpenClaw response = openclaw_agent.process(content) # Reply dingtalk_client.send_text_message( message.sender_id, response ) return AckMessage.STATUS_OK # Start Stream listener client = dingtalk_stream.DingTalkStreamClient( client_id=os.getenv('DINGTALK_CLIENT_ID'), client_secret=os.getenv('DINGTALK_CLIENT_SECRET') ) client.register_callback_handler('chatbot', message_handler) client.start_forever() ``` ### 2. Lark CLI Integration (`cn-feishu-lark-cli.md`) Agents can use Lark CLI to operate Feishu as the user: ```bash # Install Lark CLI pip install lark-cli # Configure authentication export LARK_APP_ID="your_app_id" export LARK_APP_SECRET="your_app_secret" export LARK_USER_ACCESS_TOKEN="your_token" # Search documents lark-cli docx search --keyword "项目文档" # Read meeting notes lark-cli meeting-minutes list --date 2026-05-01 # Get calendar events lark-cli calendar events --start-date 2026-05-16 --end-date 2026-05-17 # Send message lark-cli message send --user-id "ou_xxx" --text "任务已完成" ``` **OpenClaw Skill Integration**: ```markdown ## Available Tools - `lark_cli_search`: Search Feishu documents - `lark_cli_calendar`: Query calendar events - `lark_cli_message`: Send notifications ## Example Prompt Search for project documentation related to "AI Agent" in Feishu and summarize the top 3 results. ## Agent Execution 1. Run: `lark-cli docx search --keyword "AI Agent" --limit 3` 2. Parse JSON output 3. For each doc, fetch content: `lark-cli docx get --doc-id {id}` 4. Summarize and return ``` ### 3. A-Share Stock Monitoring (`cn-a-share-monitor.md`) ```python # Using AKShare (free, no API key needed) import akshare as ak from datetime import datetime def get_market_overview(): """Pre-market briefing""" # Get index data sh_index = ak.stock_zh_index_daily(symbol="sh000001") latest = sh_index.iloc[-1] # Get sector money flow sectors = ak.stock_sector_fund_flow_rank(indicator="今日") top_sectors = sectors.head(5) return { "sh_index": { "close": latest['close'], "change": latest['close'] - latest['open'], "volume": latest['volume'] }, "top_sectors": top_sectors.to_dict('records') } def post_market_review(): """Post-market analysis""" # Get individual stock rankings gainers = ak.stock_zh_a_spot_em().nlargest(10, 'pct_chg') losers = ak.stock_zh_a_spot_em().nsmallest(10, 'pct_chg') return { "gainers": gainers[['code', 'name', 'pct_chg']].to_dict('records'), "losers": losers[['code', 'name', 'pct_chg']].to_dict('records') } # Cron schedule in OpenClaw # 8:30 AM: Send pre-market briefing to Feishu # 3:30 PM: Send post-market review to Feishu ``` ### 4. Multi-Agent Architecture (`cn-multi-agent-operating-system.md`) **Core Pattern**: ```yaml # workspace/AGENTS.md structure agents: - name: coordinator role: Task decomposition and delegation memory: Global context - name: researcher role: Information gathering skills: [web-search, pdf-reader] - name: writer role: Content generation skills: [markdown-writer, seo-optimizer] - name: publisher role: Platform distribution skills: [feishu-bot, xiaohongshu-api] workflow: 1. User sends request to coordinator 2. Coordinator spawns sub-agents 3. Sub-agents report back to coordinator 4. Coordinator synthesizes final output ``` **Implementation Example**: ```javascript // Coordinator agent prompt const coordinatorPrompt = ` You are a coordinator. When given a task: 1. Break it into subtasks 2. Assign each to a specialist sub-agent: - @researcher for data collection - @writer for content creation - @publisher for distribution 3. Collect results and synthesize 4. Return final output Current task: Create and publish a Xiaohongshu post about OpenClaw `; // Spawn sub-agents const researchResult = await spawnAgent('researcher', { task: 'Find trending OpenClaw use cases', tools: ['perplexity_search', 'github_trending'] }); const content = await spawnAgent('writer', { task: 'Write Xiaohongshu post', context: researchResult, tools: ['markdown_formatter', 'emoji_suggester'] }); const published = await spawnAgent('publisher', { task: 'Publish to Xiaohongshu', content: content, tools: ['xiaohongshu_api'] }); ``` ### 5. Xiaohongshu Automation (`cn-xiaohongshu-automation.md`) ```python # Unofficial API (use with caution, rate limits apply) from xhs import XhsClient client = XhsClient( cookie=os.getenv('XHS_COOKIE'), # Get from browser ) def publish_note(title, content, images, tags): """Publish note to Xiaohongshu""" # Upload images first image_ids = [] for img_path in images: with open(img_path, 'rb') as f: result = client.upload_image(f.read()) image_ids.append(result['image_id']) # Create note note = client.create_note( title=title, desc=content, image_ids=image_ids, tags=tags, post_time=None, # Publish immediately, or set timestamp is_private=False ) return note['note_id'] # OpenClaw scheduled task # Daily 7PM: Generate trending topic post # Use DALL-E for cover image # Auto-publish with optimal hashtags ``` ### 6. WeChat Official Account (`cn-wechat-mp-automation.md`) ```python # Using wechatpy library from wechatpy import WeChatClient from wechatpy.client.api import WeChatMedia, WeChatMaterial client = WeChatClient( appid=os.getenv('WECHAT_APPID'), secret=os.getenv('WECHAT_SECRET') ) def markdown_to_wechat_html(md_content): """Convert Markdown to WeChat-styled HTML""" import markdown2 html = markdown2.markdown(md_content, extras=['fenced-code-blocks']) # Apply WeChat styling