auto-model-routing

name	auto-model-routing
category	mlops
description	Automatically select the best LLM model for a given task based on task characteristics using lightweight rules-based routing.
triggers	["before any non-trivial task when model hasn't been specified by user","when user asks for help without specifying a specific model","when a task could benefit from cost-effective model selection"]
version	1.0.0

Auto Model Routing Skill

Purpose

Automatically select the best model for a given task based on task characteristics. Uses a lightweight rules-based router — no LLM needed for routing decisions.

Trigger Conditions

Activate this skill when:

• User submits a task without specifying a model
• Task complexity is unclear and you need to choose an appropriate model
• You want to optimize for cost-performance balance

Routing Logic

Task Classification Flow

Task Text → Keyword Analysis → Category Detection → Model Selection

Model Categories

Category	Keywords	Selected Model	Provider
reasoning/heavy	debug, architect, design, analyze, complex, system	claude-sonnet-4-20250514	anthropic
fast/light	quick, simple, one-liner, small, easy, fix typo	MiniMax-M2.7-highspeed	minimax
creative	write, story, creative, song, poem, narrative	MiniMax-M2.7-highspeed	minimax
research	research, find, investigate, explore, search	deep-research-iterative	perplexity
coding	code, implement, refactor, program, build, function	claude-sonnet-4-20250514	anthropic

Output Format

{
  "selected": "model-name",
  "provider": "provider-name",
  "confidence": 0.85,
  "reason": "Matched category 'coding' via keyword 'code'"
}

Override Rules

1. User-specified model always wins — If user explicitly names a model, use that model regardless of routing rules
2. Context-aware — Consider task complexity even without explicit keywords
3. Cost awareness — Don't route trivial tasks to expensive models

Confidence Levels

Level	Score	Criteria
High	0.8-1.0	Explicit keyword match
Medium	0.5-0.79	Partial match or context inference
Low	0.3-0.49	Ambiguous, using fallback

Fallback

If no rule matches: return MiniMax-M2.7-highspeed with confidence: 0.3

Usage

CLI

python scripts/router.py "debug my Python code"

Integration

from router import route_task

result = route_task("analyze this architecture")
print(result)

Files

• scripts/router.py — Main routing engine
• references/rules.md — Detailed routing rules and model mappings