Summarize CodexBar JSONL cost logs for Codex and Claude providers.
npx skills add https://github.com/wangqianCAI/OBI --skill model-usage-wangqiancai
Please help me install this Agent Skill. Skill: model-usage Source: https://github.com/wangqianCAI/OBI/tree/main/skills/model-usage Command: npx skills add https://github.com/wangqianCAI/OBI --skill model-usage-wangqiancai
This Skill addresses the difficulty of tracking and summarizing local AI model usage costs, which are often buried in fragmented JSONL log files generated by CodexBar.
Use the model-usage skill to summarize all model costs for the Claude provider in JSON format.
High-intent search queries and answers about installing and using this skill.
To summarize local AI model cost logs, this Skill parses JSONL files generated by CodexBar to aggregate token and cost data. It provides granular spending insights by parsing these local logs to show exactly how much budget specific AI providers consume.
Yes, you can generate a spending breakdown for specific AI providers like Claude and Codex. The Skill operates on local JSONL log files to aggregate token usage and cost data, supporting both current model snapshots and comprehensive historical summaries.
Tracking API spending per model from JSONL files involves automatically parsing the local cost logs to provide a clear breakdown. This approach aggregates token and cost data, allowing developers to identify which specific models are consuming the most budget.
Yes, you need Python 3 and the CodexBar CLI installed to analyze AI usage costs. The Skill requires the CodexBar CLI to be installed and accessible in the system environment to parse the JSONL log files and generate the cost reports.
Aggregating token and cost data helps with budget monitoring by transforming fragmented JSONL log files into clear spending insights. This allows developers to generate monthly reports of their AI API spending and identify exactly which models are consuming the most budget.
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