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 AnthonyAlcaraz/agentic-graph-rag-skills --skill model-routing-selectorgit clone --depth 1 https://github.com/AnthonyAlcaraz/agentic-graph-rag-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/anthonyalcaraz/agentic-graph-rag-skills/model-routing-selector)<a href="https://agentmods.dev/skills/anthonyalcaraz/agentic-graph-rag-skills/model-routing-selector"><img src="https://agentmods.dev/badge/skills/anthonyalcaraz/agentic-graph-rag-skills/model-routing-selector/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/anthonyalcaraz/agentic-graph-rag-skills/model-routing-selector"><img src="https://agentmods.dev/badge/skills/anthonyalcaraz/agentic-graph-rag-skills/model-routing-selector.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 | $0.00156 | $0.02320 |
| Opus 5 | $0.00078 | $0.01160 |
| Sonnet 5 | $0.00031 | $0.00464 |
| Haiku 4.5 | $0.00016 | $0.00232 |
Grade A, and why
model-routing-selector 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 11d 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 — 167 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Model Routing Selector
Overview
The horizontal workflow graph (Chapters 5-6) decomposes the agent into specialized nodes: a query analyst, a retrieval strategist, a synthesis node, a validation node. Most teams start with the same frontier model powering every node. It works, and it is wildly expensive.
Selective intelligence matches model capability to task complexity. Running a 3B SLM can be 10-30x cheaper per token than its 405B sibling; at thousands or millions of invocations per day, that difference decides whether the project survives its first budget review. The chapter's key insight is that most nodes do not need frontier reasoning — a fine-tuned 3B model classifies alerts, a 3.8B Triplex model beats GPT-4o at knowledge-graph construction, and only the open-ended synthesis and causal-reasoning nodes (10-20% of invocations) reliably benefit from a frontier model.
This skill re-derives the book's DevOps assignment (Example 8-13) from a node's
required_quality bar and a per-model cost/capability catalog, so you can see
why each node gets the model it gets rather than accepting an opaque config.
When to Use
- A pipeline runs every node on one frontier model and cost is unsustainable.
- You are adding a node and need to know the cheapest model that clears its bar.
- A node's per-query difficulty varies and you are deciding static vs cascade.
- You have production traffic and want to justify a learned router.
Phrases that should invoke this skill: "which model for this node", "selective intelligence", "cheapest model that meets the bar", "static vs cascade routing", "cut inference cost", "RouteLLM", "FrugalGPT cascade".
When NOT to Use
- Single-model systems. With one node type and one model there is nothing to route.
- Choosing the graph data model (property graph vs RDF vs hypergraph) — that
is
graph-model-selector(Ch3). - Measuring a routing policy after deployment — that is
cost-performance-scorer(Ch8). This skill decides routing before you run; that skill scores what actually happened. - KV-cache / latency budgeting — that is
kv-cache-latency-budgeter(Ch8). Routing lowers cost per weight; it does not lower the concurrency ceiling.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 167 lines · 156 tokens per session scan A 7b9d19fbae1e
model-routing-selector is a skill published in the GitHub repository AnthonyAlcaraz/agentic-graph-rag-skills (10 stars, last pushed 2mo ago), licensed MIT. It adds 156 tokens to every session and 2,320 once invoked, about $0.0008 per session on Opus 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 2026-08-31.
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