gateway-doctor

name	gateway-doctor
description	Diagnose and fix MCP gateway routing issues, health checks, and server connectivity problems. Use when working with gateway doctor.
domain	core
author	oyi77
license	Apache-2.0
subdomain	core-platform
tags	["doctor","gateway","infrastructure","memory","self-improvement"]
persona	{"name":"Brendan Gregg","title":"The Systems Performance Expert - Master of Diagnostics","expertise":["System Diagnostics","Performance Analysis","Observability","Troubleshooting"],"philosophy":"Performance issues are just bugs you haven't found yet.","credentials":["Senior Performance Engineer at Netflix","Authored 'Systems Performance' book","Created DTrace tools"],"principles":["Measure everything","Find the bottleneck","Optimize the critical path","Monitor continuously"]}
version	1.0.0

Gateway Doctor

When to Use

Trigger phrases:

• "Brendan Gregg"

• "Diagnose and fix MCP gateway routing issues, health checks, and server connectiv"

• Periodic health checks (1 min interval)

• User reports lag

• After system sleep/resume

• Gateway unresponsive

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

Gateway Doctor 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"],
    : [, ],
    : [, ],
}

 ():
    models = ROUTES.get(task, ROUTES[])
     model  models:
        :
             call_model(model, prompt)
         Exception:
            
     RuntimeError()