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)

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# 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

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# 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

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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

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# 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

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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)


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