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OpenClaw五维实战案例
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OpenClaw五维实战案例｜附完整配置命令与效果数据
GitHub ⭐ 371,000 的开源 AI Agent OS，已构建 5,400+ Skills 生态。 本文精选 5 个经社区验证的实战案例，附可直接复用的配置命令与效果数据。
📰 案例一：每日AI简报自动生成
解决了什么问题：每天手动搜索行业资讯耗时2小时，OpenClaw + Tavily Web Search + Cron Job，实现每日8:00自动推送到飞书/钉钉。
Skill 组合
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完整配置命令
【黑底命令部分直接拷贝喂给OpenClaw，并告诉它根据本系统配置检查执行】
# Step 1: 安装 OpenClaw（如未安装） openclaw init my-news-brief
# Step 2: 安装所需 Skills openclaw skill install tavily-web-search openclaw skill install feishu-notifications
# Step 3: 创建简报生成 Skill（保存为 ~/.openclaw/skills/daily-brief/main.py） import asyncio from tavily import TavilyClient from datetime import datetime import os async def daily_brief(): client = TavilyClient(api_key=os.environ["TAVILY_API_KEY"]) today = datetime.now().strftime('%Y-%m-%d') # 搜索AI和大模型领域当日热点 results = client.search( query=f"AI large language model news {today}", max_results=8 ) brief = f"📰 AI日报 {datetime.now().strftime('%m月%d日')}\n\n" for i, r in enumerate(results[:5], 1): brief += f"{i}. **{r['title']}**\n {r['url']}\n\n" return brief
# Step 4: 配置定时任务（每日8:00） openclaw cron create \ --skill daily-brief \ --trigger "0 8 * * *" \ --output feishu \ --feishu-webhook $FEISHU_WEBHOOK
# Step 5: 测试运行 openclaw run daily-brief --test
效果数据
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📈 案例二：投研助手——Tushare + 智能选股
解决了什么问题：手动从Tushare拉数据、做分析、写报告需要3小时，OpenClaw Skill Chain 实现一键生成完整投研报告，含K线、均线、MACD指标图表。
Skill 组合
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完整配置命令
# Step 1: 安装 Skills openclaw skill install tushare-stock-query openclaw skill install akshare-financial openclaw skill install plot-stock-chart openclaw skill install report-writer
# Step 2: 配置 Tushare Token（免费注册：https://tushare.pro/） export TUSHARE_TOKEN=your_tushare_token_here
# Step 3: 创建投研助手（~/.openclaw/skills/investment-advisor/main.py） import asyncio, tushare as ts, os from datetime import datetime, timedelta async def investment_report(stock_code: str, days: int = 60): pro = ts.pro_api(os.environ["TUSHARE_TOKEN"]) # 获取日线数据 df = pro.daily( ts_code=stock_code, start_date=(datetime.now() - timedelta(days=days)).strftime('%Y%m%d'), end_date=datetime.now().strftime('%Y%m%d') ) # 计算均线 df['ma5'] = df['close'].rolling(5).mean() df['ma20'] = df['close'].rolling(20).mean() df['ma60'] = df['close'].rolling(60).mean() # 生成图表 chart = await plot_stock_chart(df, stock_code) # 生成报告 report = f"# {stock_code} 投研报告\n\n" report += f"**生成时间**: {datetime.now().strftime('%Y-%m-%d %H:%M')}\n\n" report += f"**最新收盘价**: ¥{df.iloc[-1]['close']}\n" report += f"**MA5/MA20/MA60**: ¥{df.iloc[-1]['ma5']:.2f} / " report += f"¥{df.iloc[-1]['ma20']:.2f} / ¥{df.iloc[-1]['ma60']:.2f}\n\n" report += f"\n\n" # 简单择时信号 if df.iloc[-1]['ma5'] > df.iloc[-1]['ma20']: report += "✅ **信号**: 短期均线多头排列，看涨\n" else: report += "⚠️ **信号**: 短期均线空头排列，谨慎\n" return report # Step 4: 运行 openclaw run investment-advisor --stock 000001.SZ # 输出：约500字完整投研报告 + K线图表
效果数据
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✍️ 案例三：内容自动化工厂
解决了什么问题：小红书/公众号内容生产需要选题→写作→配图→发布全流程，OpenClaw Multi-Agent流水线实现从热点发现到发布的全自动化，用户只需确认发布。
Multi-Agent 流水线设计
# Agent 1: 热点发现（每30分钟扫描） Agent: trend_scanner Skill: tavily-web-search + zhihu-trending Trigger: cron */30 * * * *
# Agent 2: 内容写作（收到热点后自动触发） Agent: content_writer Input: trend_scanner.output Skill: gpt-4o-write + keyword_density Style: 小红书风格（emoji+分段+悬念）
# Agent 3: 配图生成（调用MiniMax文生图） Agent: image_generator Input: content_writer.output Skill: minimax-image-gen Style: 封面图+3张内文配图，统一色调
# Agent 4: 发布审核（人工确认后发布） Agent: publisher Input: image_generator.output + 人工确认 Skill: wechat-publish + xiaohongshu-post Action: 存入草稿箱 → 通知 → 发布
完整配置命令
# Step 1: 创建内容工厂项目 openclaw init content-factory
# Step 2: 安装所需 Skills openclaw skill install tavily-web-search openclaw skill install wechat-publish openclaw skill install minimax-image-gen
# Step 3: 配置工作流（openclaw.yml） # name: content-factory # agents: # - name: trend_scanner # trigger: cron "*/30 9-22 * * *" # skills: [tavily-web-search] # # - name: content_writer # trigger: event: trend_scanner.done # skills: [gpt-4o-write] # # - name: image_generator # trigger: event: content_writer.done # skills: [minimax-image-gen] # # - name: publisher # trigger: approval # 人工审核 # skills: [wechat-publish]
# Step 4: 启动工厂 openclaw factory start content-factory # Step 5: 查看队列 openclaw factory status
效果数据
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📋 案例四：会议纪要自动化
解决了什么问题：会议录音→手动整理→分发待办，需要1-2小时，OpenClaw + Whisper + Task Extraction，实现会议结束5分钟内自动生成结构化纪要和待办任务，直接分发到飞书群。
完整配置命令
# Step 1: 安装 Skills openclaw skill install whisper-asr openclaw skill install task-extractor openclaw skill install feishu-notifications
# Step 2: 创建会议纪要 Skill # ~/.openclaw/skills/meeting-notes/main.py import asyncio, whisper async def meeting_minutes(audio_file: str, topic: str): # ASR语音转文字 model = whisper.load_model("base") result = await asyncio.to_thread( model.transcribe, audio_file, language="zh" ) transcript = result["text"] # LLM提取关键信息 summary = await gpt4o.analyze( prompt=f"""从以下会议记录中提取: 1. 会议议题 2. 关键决策（3条以内） 3. 待办任务（每条: 负责人+截止时间+任务描述） 4. 下次会议时间 会议记录: {{transcript}}""" ) # 生成飞书格式消息 msg = f"📋 **{topic} 会议纪要**\n\n" msg += f"📌 **议题**: {{summary['topic']}}\n\n" msg += "✅ **决策**:\n" for d in summary['decisions']: msg += f" • {{d}}\n" msg += "\n📍 **待办**:\n" for task in summary['tasks']: msg += f" • [ ] {{task['person']}} | {{task['deadline']}} | {{task['desc']}}\n" return msg
# Step 3: 配置 Webhook export FEISHU_WEBHOOK=https://open.feishu.cn/open-apis/bot/v2/hook/xxx # Step 4: 使用 openclaw run meeting-minutes --file ./meeting.m4a --topic "Q2产品规划评审"
效果数据
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🖥️ 案例五：自愈式服务器监控
解决了什么问题：服务器磁盘满、内存泄漏、服务宕机等突发问题，OpenClaw + 心跳监控 + SSH Command Execution，实现问题自动发现→诊断→修复→报告全链路，值班工程师半夜被叫醒的概率降低90%。
完整配置命令
# Step 1: 安装 Skills openclaw skill install server-heartbeat openclaw skill install ssh-command openclaw skill install alert-manager
# Step 2: 创建自愈 Skill # ~/.openclaw/skills/self-healing/main.py import asyncio, psutil async def health_check(): issues = [] # 1. 磁盘检查 disk = psutil.disk_usage('/') if disk.percent > 90: issues.append({ "severity": "critical", "type": "disk_full", "fix": "docker system prune -af && find /tmp -type f -mtime +7 -delete" }) # 2. 内存检查 mem = psutil.virtual_memory() if mem.percent > 85: issues.append({ "severity": "warning", "type": "memory_high", "fix": "sync && echo 3 > /proc/sys/vm/drop_caches" }) # 3. 服务健康检查 for svc in ["nginx", "openclaw", "mysql"]: if not await is_service_alive(svc): issues.append({ "severity": "critical", "type": f"service_down_{svc}", "fix": f"systemctl restart {{svc}}" }) # 4. 自动修复（critical级别自动执行） for issue in issues: if issue["severity"] == "critical": result = await ssh_execute(issue["fix"]) await alert_manager.report(issue, result) return issues
# Step 3: 配置心跳监控（每5分钟） openclaw cron create \ --skill self-healing \ --trigger "*/5 * * * *" \ --alert-level warning \ --auto-fix critical \ --notify feishu # Step 4: 查看自愈日志 openclaw logs self-healing --last 24h
效果数据
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🚀 快速上手路径
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