model-usage

You can track LLM token usage and calculate API costs by reading your log files and executing JavaScript to parse interactions and compute expenses based on model pricing and token consumption. This generates detailed usage breakdowns for budget management.

Yes, you can generate daily, monthly, and model-specific usage reports to identify high-usage models. It calculates costs by analyzing input, output, and total tokens used per session, helping you pinpoint the most expensive API interactions.

The best way to monitor API expenses is by analyzing token consumption logs to track spending patterns. This approach provides optimization tips such as selecting cheaper models and managing context size to reduce overall AI costs.

Yes, JavaScript execution is required to parse and calculate token consumption data from log files. The skill relies on executing JavaScript to read interactions, compute costs, and generate accurate API usage reports.

It works with existing LLM API interaction logs by reading the files directly to extract session data. It then analyzes token consumption and calculates costs to provide actionable budget management insights.

A limitation of using log files for LLM cost analysis is that the accuracy depends entirely on the log data available. It requires existing files containing token counts and model information to correctly calculate expenses and generate usage reports.