model-router

This skill provides a compact, runnable router that inspects an OpenClaw-style configuration (or a simple models JSON) and selects an appropriate model for an incoming request based on:

• declared model capabilities and an optional cost score
• task complexity (heuristic: short/simple vs long/complex)
• explicit overrides (user or caller hints)

Design principles

• Keep decision logic small and deterministic.
• Default to the cheapest model for general, not-complex tasks.
• Escalate to stronger models when the task appears complex or asks for high-fidelity results.
• Make model metadata explicit (capabilities, cost_score, tags) so the router is transparent and auditable.

What this skill includes

• scripts/router.py — a small CLI and library to pick a model given a task description and a models configuration file.
• examples/models.json — example model configurations (name, provider, cost_score, capabilities).

When to use

• Trigger when you need to programmatically choose which LLM to call for a user request.
• Use for batching or middleware routing in server apps.

Usage (quick)

• Prepare a models file (JSON) with model entries. See examples/models.json.
• Call: python3 scripts/router.py --models examples/models.json --task "Summarize this email" --mode auto
• The script prints the chosen model and the reasoning.

Files

• scripts/router.py — router CLI/library
• examples/models.json — sample model list