ecomode

Prioritize cheaper AI model tiers in routing workflows with configurable opt-outs and safety guards.

Updated Apr 3, 2026
One-click install
npx skills add https://github.com/pextacy/stellar --skill ecomode-pextacy
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: ecomode
Source: https://github.com/pextacy/stellar/tree/main/skills/ecomode
Command: npx skills add https://github.com/pextacy/stellar --skill ecomode-pextacy

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Token-efficient model routing reduces operational costs by prioritizing cheaper AI model tiers and escalating only when necessary.

Core Features & Use Cases

  • Tier-aware routing that prioritizes lower-cost models while preserving essential performance.
  • Delegation rules that maintain core protocol safeguards and optimize resource usage.
  • Safe opt-out and easy configuration for scenarios requiring full fidelity or custom routing.

Quick Start

Enable ecomode in your config to begin cost-aware, tiered model routing.

Frequently Asked Questions about ecomode

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
What is tier-aware AI model routing and how does it reduce costs?

Tier-aware AI model routing reduces operational costs by prioritizing cheaper model tiers and escalating to higher tiers only when necessary, preserving essential performance while optimizing resource usage.

How do I configure cost-aware delegation rules for multi-model environments?

You can enable ecomode in your configuration to begin cost-aware, tiered model routing, applying tier-aware decision logic with configurable opt-out and safety guards to ensure essential performance.

Can I opt out of cheaper model tiers when full fidelity is required?

Yes, you can use the safe opt-out and easy configuration features for scenarios requiring full fidelity or custom routing, bypassing the default cost-optimization logic.

What's the best way to optimize AI token costs without sacrificing task quality?

The best way to optimize AI token costs is implementing tier-aware routing that prioritizes lower-cost models while maintaining core protocol safeguards and applying safety guards for essential performance.

When should I not use cheaper model tiers for AI delegation?

You should not use cheaper model tiers when scenarios require full fidelity or custom routing, instead utilizing the safe opt-out configuration to bypass cost-optimization constraints.