I Tried OpenClaw for 7 Days, Decided to Migrate All Personal AI Assistants

2026-03-03

I Tried OpenClaw for 7 Days, Decided to Migrate All Personal AI Assistants

Hello everyone, this is programmer Wan Feng actively working on various AI projects.

Me 7 days ago:

❌ Using 3 different AI tools (ChatGPT, Claude, Wenxin Yiyan)
❌ Data scattered across platforms
❌ Paying $60+/month
❌ Can't automate workflows

Me after 7 days:

✅ All AI interactions unified in OpenClaw
✅ Data completely private
✅ Monthly cost $0 (using open source models)
✅ Automated 80% of repetitive work

What did I experience these 7 days? Complete experience report here.

📅 Day 1: Installation and Initialization

Installation Process (15 minutes)

# One-click install
curl -fsSL https://openclaw.ai/install.sh | bash

# Configure model
openclaw config set model claude-sonnet-4
openclaw config set api_key $YOUR_API_KEY

# Start service
openclaw start

Experience:

✅ Installation script very intelligent, auto-detects system
✅ Configuration file has detailed comments
✅ First launch has interactive guide

First Task: WeChat Auto Reply

# skills/wechat-auto-reply.yaml
trigger: "在吗" (Are you there)
response: |
  Hello! I'm the auto assistant.
  I'm away暂时不在，但可以帮你：
  1. Query information
  2. Schedule appointments
  3. Handle documents

  Please直接说你的需求~

Effect: Immediate effect after configuration, WeChat receives auto reply on "在吗".

📅 Day 2-3: Core Features Exploration

1. Multi-platform Message Unification

Before:
- WeChat messages → Phone
- Telegram messages → Computer
- Email → Webpage
- Slack messages → Another app

After OpenClaw:
All messages → Unified inbox → AI auto classification and processing

Efficiency improvement: 70% reduction in app switching

2. Scheduled Task Automation

# skills/daily-report.py
@cron("0 9 * * *")  # Every day at 9 AM
def daily_report():
    # Get yesterday's data
    stats = get_yesterday_stats()

    # Generate report
    report = ai_generate_report(stats)

    # Send to WeChat group
    send_wechat_group("日报", report)

Effect: Daily automatic team report sending, saving 30 minutes/day

3. Document Intelligent Processing

Scenario: Processing 100 PDF contracts

Traditional way:
1. Open PDF
2. Copy content
3. Paste to AI
4. Wait for analysis
5. Organize results
Time: 5 minutes/file × 100 = 500 minutes

OpenClaw way:
1. Upload folder
2. Run skill
3. Wait for completion
Time: 10 minutes (parallel processing)

Efficiency improvement: 50x

📅 Day 4-5: Deep Customization

Custom Skill Development

Requirement: Auto monitor GitHub project updates

# skills/github-monitor.py
from openclaw import Skill, trigger

@trigger(keyword="github")
class GitHubMonitor(Skill):
    def __init__(self):
        self.repos = ["openclaw/openclaw", "python/cpython"]

    async def run(self, query):
        for repo in self.repos:
            # Get latest updates
            updates = await self.get_repo_updates(repo)

            # AI analyze importance
            importance = await self.ai_analyze(updates)


## 🎓 AI 编程实战课程

想系统学习 AI 编程？程序员晚枫的 **AI 编程实战课** 帮你从零上手！

- 👉 **课程报名**：[点击这里报名，前3讲免费试听](https://r7up9.xetslk.com/s/1uP5YW)
- 👉 **免费试看**：[B站免费试看前3讲，先看看适不适合自己](https://www.bilibili.com/cheese/play/ss982042944)



            # Push notification

jsonContent: meta: false pages: false posts: title: true date: true path: true text: false raw: false content: false slug: false updated: false comments: false link: false permalink: false excerpt: false categories: false tags: true