cloudflare-router

Cloudflare Router

When to Use

Trigger phrases:

• "cloudflare router"

• "Adding new subdomains pointing to local services"

• "Managing Cloudflare Tunnel ingress rules"

• "Generating nginx reverse proxy configs"

• Adding new subdomains pointing to local services

• Managing Cloudflare Tunnel ingress rules

• Generating nginx reverse proxy configs

• Deploying DNS records to Cloudflare

• Monitoring service health and status

When NOT to Use

• When the task can be solved with existing standard libraries
• When the infrastructure is already in place and working
• When the added complexity does not provide measurable benefit

Overview

Cloudflare Router is a foundational core infrastructure skill that provides system foundation capabilities for the agent ecosystem.

Architecture

• Input layer — Receives and validates incoming requests
• Processing layer — Core logic for system foundation
• Output layer — Formats and delivers results
• State management — Maintains context across invocations

Configuration

• Set up required environment variables and paths
• Configure logging level and output format
• Define resource limits (memory, time, API calls)
• Enable/disable features via configuration flags

Integration

• Exposes standard interfaces for other skills to consume
• Supports event-driven and request-response patterns
• Compatible with the 1ai-skills hook system
• Logs metrics for the skill performance monitor

Anti-Rationalization Table

Rationalization	Reality
"I will add monitoring later"	Without monitoring, you cannot detect failures. Add it from day one.
"One model is enough"	Different tasks need different models. Route intelligently.
"Premature optimization"	Infrastructure decisions are hard to change later. Design for scale early.

# Example: Model routing
ROUTES = {
    "code": ["claude-sonnet-4-20250514", "gpt-4o"],
    "vision": ["gemini-2.5-pro", "gpt-4o"],
    "fast": ["gemini-2.5-flash", "gpt-4o-mini"],
}

def route_request(task: str, prompt: str):
    models = ROUTES.get(task, ROUTES["fast"])
    for model in models:
        try:
            return call_model(model, prompt)
        except Exception:
            continue
    raise RuntimeError("All models failed")