/route — Smart LLM Task Router

Route any task to the optimal LLM automatically.

Usage

/route <task description>

Auto-Classification

Most prompts are classified automatically by the UserPromptSubmit hook — no /route needed. The hook uses a multi-layer classification chain:

1. Heuristic scoring (instant, free) — Three signal layers accumulate evidence:

   • Intent patterns (+3) — action verbs and task markers
   • Topic patterns (+2) — domain-specific nouns
   • Format patterns (+1) — structural and temporal cues
   • High-confidence match (score >= 4) routes immediately

2. Ollama local LLM (~1s, free) — When heuristics are uncertain, qwen3.5 classifies locally via the chat API with thinking disabled

3. Cheap API model (~$0.0001) — If Ollama is unavailable, Gemini Flash or GPT-4o-mini classifies

4. Weak heuristic / auto fallback — Last resort: low-confidence heuristic match or llm_route (full LLM classifier)

Task Categories

Category	Tool	Signals
Research	llm_research	Current events, news, funding, trends, market data, rankings
Generate	llm_generate	Writing, drafting, brainstorming, emails, articles, translations
Analyze	llm_analyze	Evaluation, debugging, comparison, trade-offs, code review
Code	llm_code	Implementation, refactoring, building, bug fixes
Query	llm_query	Simple questions, definitions, explanations
Image	llm_image	Visual generation, design, artwork

Complexity & Profiles

Complexity	Profile	Model Tier
Simple	budget	Gemini Flash, GPT-4o-mini
Moderate	balanced	GPT-4o, Gemini 2.5 Pro
Complex	premium	o3, Gemini 2.5 Pro

Savings Awareness

Every 5th routed task, the system shows estimated savings: Claude API costs avoided and rate limit capacity preserved. Run llm_usage for a detailed breakdown.

Examples

What are the top 3 AI startups that raised funding?
→ research (heuristic, score=8) → llm_research (budget) → Perplexity Sonar

Write me a blog post about productivity tips
→ generate (heuristic, score=5) → llm_generate (balanced) → Gemini 2.5 Pro

Compare React vs Vue for our new project
→ analyze (ollama, qwen3.5) → llm_analyze (balanced) → GPT-4o

Implement a rate limiter in Python using sliding window
→ code (heuristic, score=4) → llm_code (balanced) → GPT-4o

What is a monad?
→ query (ollama, qwen3.5) → llm_query (budget) → Gemini Flash

Configuration

Environment variables:

• LLM_ROUTER_OLLAMA_MODEL — Ollama model (default: qwen3.5:latest)
• LLM_ROUTER_OLLAMA_URL — Ollama server (default: http://localhost:11434)
• LLM_ROUTER_OLLAMA_TIMEOUT — Timeout in seconds (default: 5)
• LLM_ROUTER_CONFIDENCE_THRESHOLD — Heuristic score cutoff (default: 4)