model-hierarchy-skill

Cost-optimize AI agent operations by routing tasks to appropriate models based on complexity.

The Problem

Most AI agents run everything on expensive models. But 80% of agent tasks are routine: file reads, status checks, formatting, simple Q&A. You're paying $15-75/M tokens for work that $0.14/M tokens handles fine.

The Solution

A skill that teaches agents to classify tasks and route them to the cheapest model that can handle them:

Task Type	Model Tier	Cost	Examples
Routine (80%)	Cheap	$0.14-0.50/M	File ops, status checks, formatting
Moderate (15%)	Mid	$1-5/M	Code gen, summaries, drafts
Complex (5%)	Premium	$10-75/M	Debugging, architecture, novel problems

Result: ~10x cost reduction with equivalent quality on the tasks that matter.

Quick Start

OpenClaw

# Copy SKILL.md to your skills directory
cp SKILL.md ~/.openclaw/skills/model-hierarchy/SKILL.md

# Restart gateway to pick up the skill
openclaw gateway restart

Claude Code / Codex

Add to your CLAUDE.md or project instructions:

## Model Routing

Before executing tasks, classify complexity:
- ROUTINE (file ops, lookups, formatting) → Use cheapest model
- MODERATE (code, summaries, analysis) → Use mid-tier model  
- COMPLEX (debugging, architecture, failures) → Use premium model

When spawning sub-agents, default to cheap models unless task requires more.

Other Agent Systems

See SKILL.md for the full classification rules and integration examples.

Cost Math

Assuming 100K tokens/day:

Strategy	Monthly Cost
Pure Opus	~$225
Pure Sonnet	~$45
Hierarchy (80/15/5)	~$19

Testing

# Run classification tests
python -m pytest tests/ -v

# Test specific scenarios
python tests/test_classification.py

Files

model-hierarchy-skill/
├── SKILL.md          # The skill (install this)
├── README.md         # You're here
├── tests/
│   ├── test_classification.py
│   └── scenarios.json
└── examples/
    ├── openclaw.md
    └── claude-code.md

License

MIT