Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add itallstartedwithaidea/agent-skills --skill multi-model-routinggit clone --depth 1 https://github.com/itallstartedwithaidea/agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/itallstartedwithaidea/agent-skills/multi-model-routing)<a href="https://agentmods.dev/skills/itallstartedwithaidea/agent-skills/multi-model-routing"><img src="https://agentmods.dev/badge/skills/itallstartedwithaidea/agent-skills/multi-model-routing/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/itallstartedwithaidea/agent-skills/multi-model-routing"><img src="https://agentmods.dev/badge/skills/itallstartedwithaidea/agent-skills/multi-model-routing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00034 | $0.02728 |
| Opus 5.5 | $0.00014 | $0.01091 |
| Sonnet 5.5 | $0.00007 | $0.00546 |
| Haiku 4.5 | $0.00003 | $0.00273 |
Grade A, and why
multi-model-routing scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 237 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Multi-Model Routing
Part of Agent Skills™ by googleadsagent.ai™
Description
Multi-Model Routing is the intelligent dispatch of agent tasks to the optimal model provider based on task characteristics, cost constraints, latency requirements, and availability. Production AI systems that rely on a single model provider are fragile and expensive. Multi-Model Routing creates a resilient, cost-efficient agent architecture that leverages the strengths of Claude, GPT, Gemini, and open-source models, automatically selecting the best model for each task and failing over gracefully when a provider is unavailable.
This skill documents the multi-model routing architecture powering the Buddy™ agent at googleadsagent.ai™, which routes between Claude (primary — strongest reasoning), GPT-4o (secondary — strong function calling), and Gemini (tertiary — large context, low cost) based on task classification. The routing layer reduced costs by 45% compared to using Claude for all tasks while maintaining equivalent quality scores, because many subtasks (formatting, summarization, data extraction) perform identically on cheaper models.
The routing decision incorporates four factors: model strengths (code reasoning, long context, structured output, creative writing), cost per token (varies 100x between model tiers), latency targets (real-time vs. batch), and availability (rate limits, outages, degraded performance). A circuit breaker pattern ensures that temporary provider issues don't cascade into user-facing failures.
Use When
- Monthly AI costs need reduction without sacrificing quality
- You need resilience against single-provider outages or rate limits
- Different subtasks have fundamentally different model requirements
- Latency-sensitive and latency-tolerant tasks coexist in the same system
- You want to evaluate new models without fully committing to them
- Compliance requires not being locked into a single AI vendor
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 237 lines · 34 tokens per session scan A baa23f470f18
multi-model-routing is a skill published in the GitHub repository itallstartedwithaidea/agent-skills (44 stars, last pushed 6mo ago), licensed MIT. It adds 34 tokens to every session and 2,728 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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