zscole/model-hierarchy-skill

Registry 信息

项目简介

一个用于成本优化模型路由的Claude Skill，能根据任务复杂度智能地将任务分配给不同的AI模型，帮助平衡性能与开销。

要点

• 成本优化的模型路由
• 任务复杂度评估
• 智能模型选择
• Python实现

使用场景

• 降低AI推理成本
• 自动化模型选择
• 处理混合复杂度工作负载
• 优化资源分配

README 摘要

# 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 ```bash # 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: ```markdown ## 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](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 ```bas

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