| 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
- User-specified model always wins — If user explicitly names a model, use that model regardless of routing rules
- Context-aware — Consider task complexity even without explicit keywords
- 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 enginereferences/rules.md — Detailed routing rules and model mappings
