cheap-pinner

Pin requests to the cheapest sufficient model version for cost efficiency.

Updated May 11, 2026

Search Tags:#efficiency #cost-optimization #identity #token-management #ai-harness #model-pinning

Ready to run

npx skills add https://github.com/AesopScott/mojo --skill cheap-pinner

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: cheap-pinner
Source: https://github.com/AesopScott/mojo/tree/main/harnesses/skills/cheap-pinner
Command: npx skills add https://github.com/AesopScott/mojo --skill cheap-pinner

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill addresses excessive operational costs and token waste by enforcing the use of the most cost-efficient model version that still meets your specific capability requirements.

Core Features & Use Cases

Cost Efficiency Control: Automatically pins requests to the cheapest sufficient model version.
Reproducibility: Prevents silent model swaps by locking in specific provider and version configurations.
Use Case: When managing an Identity harness, use this skill to bound token usage and compute waste by downgrading to a smaller, adequate model for non-critical tasks.

Quick Start

Use the cheap-pinner skill to analyze the current Identity harness configuration and identify the most cost-effective model version for the task.

Frequently Asked Questions about cheap-pinner

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

FAQPage Schema

How do I minimize AI operational costs without losing performance?▼

To minimize AI operational costs, you can pin requests to the cheapest sufficient model version that still meets your required performance standards. This prevents compute waste by using smaller, adequate models for non-critical tasks.

What is model pinning in AI harness management?▼

Model pinning in AI harness management locks in specific provider and version configurations to prevent silent model swaps. This enforces reproducibility and ensures requests use the most cost-efficient model version available.

How do I stop silent model swaps from causing token waste?▼

To stop silent model swaps and token waste, apply model pinning rules to lock in specific provider configurations. This controls model selection and bounds token usage by maintaining consistent, cost-efficient versions.

Can I downgrade to a smaller model for non-critical identity harness tasks?▼

Yes, you can downgrade to a smaller, adequate model for non-critical identity harness tasks. This bounds token usage and minimizes operational expenditure by applying model pinning rules to route requests efficiently.

What do I need to configure before applying cost-efficiency thresholds?▼

Before applying cost-efficiency thresholds, you need to configure model pinning rules within your AI harness. This setup validates cost boundaries and ensures the selected model version meets your capability requirements.