Extract per-model usage costs from CodexBar cost logs into JSON summaries.
npx skills add https://github.com/MOODMNKY-LLC/mnky-atlas --skill model-usage-moodmnky-llc
Please help me install this Agent Skill. Skill: model-usage Source: https://github.com/MOODMNKY-LLC/mnky-atlas/tree/main/skills/model-usage Command: npx skills add https://github.com/MOODMNKY-LLC/mnky-atlas --skill model-usage-moodmnky-llc
Manually parsing raw CodexBar cost logs to identify which AI models (Codex or Claude) are driving the highest usage costs is tedious and time-consuming, especially for teams tracking AI API spend.
Use the model-usage skill to generate a full per-model cost breakdown for all Claude usage from your local CodexBar logs.
High-intent search queries and answers about installing and using this skill.
You can track AI API spend by using a bundled Python script to process CodexBar local cost logs, extracting and aggregating per-model usage costs into a human-readable cost summary for Codex and Claude providers.
The best way to get a per-model cost breakdown is to process the raw cost JSON output from CodexBar logs, which extracts and aggregates usage costs by individual AI model for both current and historical summaries.
Summarizing AI model usage costs requires the CodexBar CLI to be installed and accessible in your environment, as the Skill relies on this tool to retrieve the raw cost logs needed for processing.
Yes, you can view both current most-recent model cost breakdowns and full historical all-models cost summaries, allowing you to analyze expensive AI model usage patterns over time for individual developers and teams.
Manually parsing CodexBar cost logs is tedious and time-consuming because raw cost JSON output lacks aggregation, making it difficult to identify which Codex or Claude AI models are driving the highest usage costs.
No, you do not need to manually sift through raw JSON data to optimize AI tooling spend; the processing logic automatically converts raw cost logs into structured JSON or human-readable cost summaries.
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