I’ve been using Claude Code—Anthropic’s CLI tool—to manage my fleet of servers since last year, and it’s genuinely changed how I work with infrastructure. This is what AI should be doing: not chatting, but actually getting things done.
My setup
9 overseas VPS instances (AWS / DMIT / Tencent Cloud), distributed across the US, Hong Kong, Japan, and Singapore, running:
- Proxy nodes (Xray + Hysteria2)
- Quantitative trading strategies
- Monitoring stack (Prometheus + Grafana)
- Various automation scripts
I used to SSH into each machine manually. Now I just tell Claude Code “update the Xray config on the Japan machine,” and it handles the SSH connection, modifies the config, restarts the service, and verifies it’s working.
The Skill system: giving AI domain expertise
Claude Code lets you define custom Skills (think of them like system prompts for specific contexts). I’ve built Skills for different task domains:
Infrastructure operations Skill
# Server Operations Assistant
You manage these VPS instances:
- aws-us: 13.x.x.x (US)
- aws-hk: 43.x.x.x (Hong Kong)
- aws-jp: 3.x.x.x (Japan)
SSH port is 18022 uniformly, username ubuntu, PEM key authentication.
Standard procedure:
1. Check if the machine is online before connecting (ping test)
2. Back up config files before making changes
3. Verify service status after changes
4. Roll back immediately if anything breaks
Now Claude has my infrastructure context built in—no need to repeat details every time.
Quantitative operations Skill
A dedicated Skill for managing trading strategies that includes:
- How to check strategy status
- Common error handling procedures
- Steps for adjusting leverage
With these Skills in place, I usually just say a sentence or two to Claude, and it figures out what to do from there.
Real-world workflow examples
Scenario 1: Strategy alert at 3am
Telegram notification: the quant machine’s memory usage hit 95%.
Before: I’d have to get up, SSH in, read logs, diagnose, and fix.
Now: Claude analyzes the alert automatically (I’ve set up workflows), and either fixes common issues directly or only wakes me for complex ones.
Scenario 2: Deploying a new machine
Just bought a new DMIT VPS. I tell Claude: “Deploy this machine with the standard configuration.”
Claude will:
- SSH in
- Update the system
- Install dependencies
- Deploy Xray + Hysteria2
- Configure the firewall
- Test connectivity
- Add the new node to the mihomo config
I just watch it happen. When it needs a decision, it asks. The whole process is 5-10x faster than doing it manually, with fewer mistakes.
Scenario 3: Debugging network issues
A node stops responding. I say: “Help me troubleshoot connectivity on aws-hk.”
Claude will:
- Test from multiple angles (TCP/UDP/DNS)
- Check system logs
- Review firewall rules
- Give me a diagnosis and repair steps
Cost efficiency
Claude Code uses the Claude API with pay-as-you-go pricing. I use Copilot Pro+ (subscription), which has usage limits.
A few ways I keep costs down:
- Good Skills save tokens: Better context means fewer conversation turns
- Batch operations: Combine multiple tasks into one conversation
- Right-size the model: Not everything needs the most powerful model
My monthly Claude Code usage cost stays reasonable relative to the time it saves me.
Tips for getting started
- Start small: Ask Claude to write a script or look up documentation—build confidence gradually
- Write detailed Skill contexts: Give it your environment details, the more the better
- Keep reviewing results: AI makes mistakes; verify before and after critical operations
- Use plan mode for complex work: Let it plan first, you review, then execute
Want to talk Claude Code tips or AI-powered operations workflows? Find me on Telegram or @panshi8848 on X.
Further reading:
- Claude API cost guide — controlling API usage and expenses
- What is OpenClaw — an AI toolchain integration platform
- Monitoring Pionex bots with Claude Code — build a practical quant monitoring script in 30 minutes
- Writing Binance futures risk controls with Claude — get alerts 3 hours before liquidation
One of the best places to practice with AI tools is quantitative trading—it combines real code needs with ongoing maintenance and optimization. If you’re new to automated trading, Pionex is the easiest entry point: 16 built-in bots, no code required.
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