Summarize per-model CodexBar usage and costs from local logs.
npx skills add https://github.com/jonnytacos8/closed_claw --skill model-usage-jonnytacos8
Please help me install this Agent Skill. Skill: model-usage Source: https://github.com/jonnytacos8/closed_claw/tree/main/skills/model-usage Command: npx skills add https://github.com/jonnytacos8/closed_claw --skill model-usage-jonnytacos8
CodexBar cost usage data can be scattered across local logs; this skill aggregates per-model usage and cost into a concise view, enabling quick cost insights.
Execute the model_usage.py script to produce per-model usage and cost summaries from local CodexBar logs.
High-intent search queries and answers about installing and using this skill.
To summarize per-model CodexBar usage and costs, execute the model_usage.py script to aggregate local JSON log data into a concise cost breakdown. It reads directly from local CodexBar logs or STDIN to provide current-model or all-model insights.
Getting a cost breakdown for Codex and Claude models involves aggregating scattered local log data into per-model summaries. This skill processes local CodexBar logs to identify the most expensive models on a given day and aggregate costs across all models.
Yes, you can filter CodexBar usage summaries by date and output them as JSON. The script supports flexible inputs from local JSON logs or STDIN, allowing optional date filtering and both JSON and text output formats for cost analysis.
The per-model cost summary tool works without external dependencies, relying solely on local scripts and references. It reads existing CodexBar cost logs directly from your local environment or via STDIN to generate usage insights.
CodexBar costs are often scattered across local logs, making aggregation difficult. You can aggregate them by running a script that parses these local JSON logs to produce a concise, per-model view of usage and expenses.
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