ypollak2 LLM Router — Smart Routing Skill
Route tasks to the cheapest capable model automatically using llm_router MCP tools.
When to Use This Skill
Before answering research, code, writing, or analysis tasks — call the appropriate llm_router tool instead of answering directly. The router picks the cheapest model that can handle the task (Ollama → Codex → paid APIs in free-first order).
Task → Tool Mapping
| Task type | Tool to call | Why |
|---|---|---|
| Simple factual question | llm_query |
Gemini Flash / Groq — 50× cheaper than o3 |
| Research / current events | llm_research |
Perplexity (web-grounded, not stale) |
| Writing / summaries / brainstorm | llm_generate |
Gemini Flash / Haiku |
| Deep analysis / debugging | llm_analyze |
GPT-4o / Gemini Pro |
| Code generation / refactoring | llm_code |
Ollama → Codex built-in → o3 |
| Don't know which type | llm_auto |
Auto-classifies + routes, tracks savings |
How to Call
llm_query(prompt="What is the capital of France?")
llm_code(prompt="Refactor this function to use async/await", complexity="moderate")
llm_research(prompt="What changed in Python 3.13?")
llm_auto(prompt="<the full user request>") # safest default
When NOT to Route
- File read/write/edit operations — execute directly
- Inline tool calls where you have the answer already
- Tasks under 5 words (likely follow-ups, not standalone requests)
Cost Impact
Routing simple tasks to Gemini Flash instead of o3 saves ~50–100×.
llm_auto shows cumulative savings every 5 calls automatically.
Run llm_savings anytime to see your totals.