Analyze local JSON logs to summarize token usage and costs for Codex or Claude models.
npx skills add https://github.com/NJX-njx/opensoul --skill model-usage-njx-njx
Please help me install this Agent Skill. Skill: model-usage Source: https://github.com/NJX-njx/opensoul/tree/main/skills/model-usage Command: npx skills add https://github.com/NJX-njx/opensoul --skill model-usage-njx-njx
This Skill enables users to track and analyze per-model token consumption and cost data from local logs, facilitating usage optimization and cost management.
Fetch cost logs via CodexBar CLI or from a JSON file, then run the script to generate a summary in plain text or JSON format.
High-intent search queries and answers about installing and using this skill.
To analyze token usage and costs from local JSON logs, you can run a Python script that reads your JSON cost log file or standard input to compute and summarize per-model consumption data. The script generates a summary in plain text or JSON format.
Yes, you can filter model cost breakdowns by recent days. The analysis script supports filtering by specific time ranges, allowing you to evaluate which models incur the highest costs over a selected period for budget optimization.
Yes, this tool works with both Codex and Claude model logs. It analyzes local JSON cost logs to compute and summarize token usage and associated costs specifically for Codex or Claude models, providing model-specific cost insights.
The best way to track per-model API costs for budget optimization is to parse local JSON logs with a Python script that computes token usage and generates detailed cost breakdowns per model, enabling you to identify high-cost models over specific time ranges.
You need a Python environment to execute the token usage analysis script. The script processes a JSON cost log input, which can be fetched via CodexBar CLI or provided from a JSON file, to output usage and cost summaries.
The cost summary requires a JSON file as input because the analysis script parses structured JSON log data to accurately compute token counts and associated costs per model. This structured format ensures precise tracking and breakdown of usage metrics.
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