model-usage

Summarize per-model CodexBar cost data from local logs.

1|Updated Jan 28, 2026
One-click install
npx skills add https://github.com/Zentin-L/Masterbot --skill model-usage-zentin-l
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: model-usage
Source: https://github.com/Zentin-L/Masterbot/tree/main/skills/model-usage
Command: npx skills add https://github.com/Zentin-L/Masterbot --skill model-usage-zentin-l

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

CodexBar cost data is generated locally; this skill provides a concise, per-model cost summary to help optimize spending and compare models.

Core Features & Use Cases

  • Current-model snapshot: quickly identify the most expensive active model for a provider (Codex or Claude) using the latest daily entry.
  • All-model breakdown: compute total costs by model across all available daily rows to inform cost-aware decisions.
  • Flexible inputs: accepts direct CodexBar JSON payloads, files, or reads from the CodexBar CLI on PATH.

Quick Start

Run the model_usage.py script with appropriate flags to generate a per-model cost report for a chosen provider.

Frequently Asked Questions about model-usage

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

FAQPage Schema
How do I summarize per-model CodexBar costs from local logs?▼

You can break down per-model CodexBar costs by applying the model_usage script to local JSON payloads or CLI data, producing either human-readable text or JSON spending summaries.

How do I find the most expensive active model in my CodexBar cost data?▼

You can identify the most expensive active model by generating a current-model snapshot from the latest daily CodexBar entry to reveal the top spending model for a chosen provider.

Does the CodexBar cost summarizer require the CLI to be installed?▼

The CodexBar cost summarizer supports direct JSON payloads or files as input, but can also read from a locally installed CodexBar CLI if it is available on your PATH.

Can I limit the per-model cost breakdown to just the last few days?▼

You can limit the per-model cost breakdown to recent days by applying an optional day limiting parameter when running the model_usage script against your local CodexBar logs.

What is the best way to compare total spending across all available AI models?▼

To compare total spending across all models, compute an all-model breakdown that aggregates total costs by model across all available daily rows in your local CodexBar logs.