通过根据复杂度将任务分配到合适的模型，优化AI代理运营的成本。

问题

大多数AI代理都用昂贵的模型来运行所有操作。但80%的代理任务都是例行的：文件读取、状态检查、格式化、简单的问答。你花15-75美元/百万代币做的工作，0.14美元/百万代币就能完成。

解决方案

一项技能教代理分类任务，并将其路由到最便宜且能处理这些任务的模型：

结果：~成本降低10倍，且在重要任务上保持同等质量。

快速启动

开爪

克劳德密码 / 密码本

在你的CLAUDE.md或项目说明中添加：

其他代理系统

完整的分类规则和积分示例请参见 SKILL.md。

成本数学

假设每天有10万代币：

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:

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:

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