How do I install the Model Gateway Routing skill?

Run npx skillmds@latest add claude-dev-suite/model-gateway-routing in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Model Gateway Routing safe to use?

This skill has not completed SkillMD's automated safety review yet. Independent scanners report: SkillSpector: PASS, Skill Scanner: PASS. Capability flags: reads secrets. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Model Gateway Routing?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Model Gateway Routing free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Model Gateway Routing?

claude-dev-suite (@claude-dev-suite) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Model Gateway Routing by claude-dev-suite

Name: Model Gateway Routing
Rating: 4.2 (5 reviews)
Author: claude-dev-suite

Model Gateway Routing

Model gateway / LLM router architecture: a control point in front of multiple models/providers for routing (cost/quality/latency), fallback, rate limiting, caching, observability, and governance. Architect-level, multi-provider. USE WHEN: designing an LLM gateway/router, "model router", "LLM gateway", "multi-provider", "fallback", "cost routing", "LiteLLM", "Envoy AI Gateway", semantic cache, central key/quota/observability for LLM calls. DO NOT USE FOR: single-engine serving (use `inference-serving-topology`); edge/cascade (use `hybrid-edge-cloud`); agent orchestration (use `agentic-architecture`).

claude-dev-suite Updated Sep 8, 2026 28 repo stars

File contents

Model Gateway / Routing

A gateway is a single control point between apps and many models/providers. It turns "which model?" and cross-cutting concerns into infrastructure.

What it centralizes (the reasons to build/buy one)

Routing: pick a model per request by cost / quality / latency / context length / capability, or A/B and canary new models.
Fallback & resilience: retry/failover across providers on error or rate limit; circuit-break a failing provider.
Cost control: per-team/app budgets, quotas, and cost attribution; route cheap queries to cheap models.
Caching: exact + semantic cache to skip duplicate/near-duplicate calls.
Security/governance: central API-key custody, PII redaction, audit logs, policy (which teams may call which models).
Observability: latency/tokens/cost/error metrics in one place.

Implementations: LiteLLM, Envoy AI Gateway, cloud AI gateways, or custom.

Design decisions

Routing policy: static (rules) vs learned/heuristic (route by predicted difficulty). Keep it explainable; mind added hop latency.
Sync vs streaming: must pass through token streaming with low overhead.
Statelessness: keep the gateway stateless + horizontally scalable; push state (cache, budgets) to fast stores.
Failure semantics: define what happens when all providers fail.

When to recommend

Multiple models/providers, multiple teams, real cost/governance needs → yes.
Single model, single team, prototype → a gateway is premature; call the model directly and add the gateway when the second model/provider/team appears.

---name: model-gateway-routingdescription: Model gateway / LLM router architecture: a control point in front of multiple models/providers for routing (cost/quality/latency), fallback, rate limiting, caching, observability, and governance. Architect-level, multi-provider. USE WHEN: designing an LLM gateway/router, "model router", "LLM gateway", "multi-provider", "fallback", "cost routing", "LiteLLM", "Envoy AI Gateway", semantic cache, central key/quota/observability for LLM calls. DO NOT USE FOR: single-engine serving (use `inference-serving-topology`); edge/cascade (use `hybrid-edge-cloud`); agent orchestration (use `agentic-architecture`).---# Model Gateway / RoutingA gateway is a single control point between apps and many models/providers.It turns "which model?" and cross-cutting concerns into infrastructure.## What it centralizes (the reasons to build/buy one)- **Routing**: pick a model per request by **cost / quality / latency / context length / capability**, or A/B and canary new models.- **Fallback & resilience**: retry/failover across providers on error or rate limit; circuit-break a failing provider.- **Cost control**: per-team/app budgets, quotas, and **cost attribution**; route cheap queries to cheap models.- **Caching**: exact + **semantic cache** to skip duplicate/near-duplicate calls.- **Security/governance**: central API-key custody, PII redaction, audit logs, policy (which teams may call which models).- **Observability**: latency/tokens/cost/error metrics in one place.Implementations: **LiteLLM**, **Envoy AI Gateway**, cloud AI gateways, or custom.## Design decisions- **Routing policy**: static (rules) vs learned/heuristic (route by predicted difficulty). Keep it explainable; mind added hop latency.- **Sync vs streaming**: must pass through token streaming with low overhead.- **Statelessness**: keep the gateway stateless + horizontally scalable; push state (cache, budgets) to fast stores.- **Failure semantics**: define what happens when all providers fail.## When to recommend- Multiple models/providers, multiple teams, real cost/governance needs → yes.- Single model, single team, prototype → a gateway is premature; call the model directly and add the gateway when the second model/provider/team appears.

Frequently asked questions