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 curiositech/some_claude_skills --skill llm-routergit clone --depth 1 https://github.com/curiositech/some_claude_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/curiositech/some_claude_skills/llm-router)<a href="https://agentmods.dev/skills/curiositech/some_claude_skills/llm-router"><img src="https://agentmods.dev/badge/skills/curiositech/some_claude_skills/llm-router/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/curiositech/some_claude_skills/llm-router"><img src="https://agentmods.dev/badge/skills/curiositech/some_claude_skills/llm-router.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00121 | $0.01820 |
| Opus 5 | $0.00060 | $0.00910 |
| Sonnet 5 | $0.00024 | $0.00364 |
| Haiku 4.5 | $0.00012 | $0.00182 |
Grade A, and why
llm-router 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 7d 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 — 170 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LLM Router
Selects the optimal LLM model for each task. The single biggest cost lever in multi-agent systems — intelligent routing saves 45-85% while maintaining 95%+ of top-model quality.
When to Use
✅ Use for:
- Deciding which model to call for a specific task
- Assigning models to DAG nodes in agent workflows
- Optimizing LLM API costs across a system
- Building cascading try-cheap-first patterns
❌ NOT for:
- Prompt engineering (use
prompt-engineer) - Model fine-tuning or training
- Comparing model architectures (academic research)
Routing Decision Tree
flowchart TD
A{Task type?} -->|Classify / validate / format / extract| T1["Tier 1: Haiku, GPT-4o-mini (~$0.001)"]
A -->|Write / implement / review / synthesize| T2["Tier 2: Sonnet, GPT-4o (~$0.01)"]
A -->|Reason / architect / judge / decompose| T3["Tier 3: Opus, o1 (~$0.10)"]
T1 --> Q1{Quality sufficient?}
Q1 -->|Yes| Done1[Use cheap model]
Q1 -->|No| T2
T2 --> Q2{Quality sufficient?}
Q2 -->|Yes| Done2[Use balanced model]
Q2 -->|No| T3
Tier Assignment Table
| Task Type | Tier | Models | Cost/Call | Why This Tier |
|---|---|---|---|---|
| Classify input type | 1 | Haiku, GPT-4o-mini | ~$0.001 | Deterministic categorization |
| Validate schema/format | 1 | Haiku, GPT-4o-mini | ~$0.001 | Mechanical checking |
| Format output / template | 1 | Haiku, GPT-4o-mini | ~$0.001 | Structured transformation |
| Extract structured data | 1 | Haiku, GPT-4o-mini | ~$0.001 | Pattern matching |
| Summarize text | 1-2 | Haiku → Sonnet | ~$0.001-0.01 | Short summaries: Haiku; nuanced: Sonnet |
| Write content/docs | 2 | Sonnet, GPT-4o | ~$0.01 | Creative quality matters |
| Implement code | 2 | Sonnet, GPT-4o | ~$0.01 | Correctness + style |
| Review code/diffs | 2 | Sonnet, GPT-4o | ~$0.01 | Needs judgment, not just pattern matching |
| Research synthesis | 2 | Sonnet, GPT-4o | ~$0.01 | Multi-source reasoning |
| Decompose ambiguous problem | 3 | Opus, o1 | ~$0.10 | Requires deep understanding |
| Design architecture | 3 | Opus, o1 | ~$0.10 | Complex system reasoning |
| Judge output quality | 3 | Opus, o1 | ~$0.10 | Meta-reasoning about quality |
| Plan multi-step strategy | 3 | Opus, o1 | ~$0.10 | Long-horizon planning |
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.
- 7d ago First seen · 170 lines · 121 tokens per session scan A fc1d57acf95b
llm-router is a skill published in the GitHub repository curiositech/some_claude_skills (219 stars, last pushed 4d ago), licensed MIT. It adds 121 tokens to every session and 1,820 once invoked, about $0.0006 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 2026-09-03.
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