zscole

zscole / model-hierarchy-skill

Public

OpenClaw skill for cost-optimized model routing based on task complexity

326
20
100% credibility
Found Feb 12, 2026 at 200 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

Instructions and a skill file that teach AI agents to sort tasks by difficulty and assign them to the most cost-effective helpers for major savings.

How It Works

1
💡 Discover Smart Savings

You hear about a clever trick to make your AI assistant cheaper by using basic helpers for easy jobs.

2
💰 Spot the Big Win

You get excited seeing how it can cut your AI costs by 10 times while keeping top quality.

3
📖 Grab the Guide

You read the friendly instructions that fit your AI setup perfectly.

4
Pick Your Path
📋
Quick Paste

Copy a ready note into your AI's special folder.

✏️
Note It Down

Add simple rules to your AI's instruction page.

5
🔄 Wake It Up

You refresh your AI so it learns the new cost-saving habits.

6
🧪 Give It a Spin

You test with everyday tasks and watch it pick the right helpers automatically.

🎉 Bills Slashed!

Your AI runs smoother and cheaper, saving you tons of money every month.

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Star Growth

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AI-Generated Review

What is model-hierarchy-skill?

This Python OpenClaw skill routes AI agent tasks to cost-optimized models based on complexity: routine file ops and formatting to cheap tiers at $0.14/M tokens, moderate code gen to mid-range, and complex debugging to premium. It tackles the waste of running everything on $15-75/M models when 80% of tasks need far less power. Drop the skill into OpenClaw skill hub, restart the gateway, or add rules to Claude projects for immediate hierarchy-based routing.

Why is it gaining traction?

It delivers ~10x cost reductions with clear math for 100K tokens/day—down to $19/month versus $225 on premium alone—while maintaining quality via task classification. OpenClaw skills integration is dead simple, plus pytest for testing routing scenarios gives confidence without deep setup. Developers hook on the practical savings for production agents, not gimmicks.

Who should use this?

AI agent builders on OpenClaw handling routine tasks like status checks or summaries at scale. Teams with Claude Code workflows spawning sub-agents, where token costs balloon from overkill models. Python devs optimizing multi-model pipelines for cost without sacrificing complex task performance.

Verdict

Grab it if you're on OpenClaw chasing cost-optimized model routing—64 stars and 1.0% credibility signal early stage, but strong docs, tests, and MIT license make it low-risk to prototype. Scale cautiously until more adoption.

(178 words)

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