Extract and summarize per-model usage costs from CodexBar cost logs.
npx skills add https://github.com/majunbao/learn --skill model-usage-majunbao
Please help me install this Agent Skill. Skill: model-usage Source: https://github.com/majunbao/learn/tree/main/openclaw_tags/openclaw-2026.3.2/skills/model-usage Command: npx skills add https://github.com/majunbao/learn --skill model-usage-majunbao
CodexBar cost data is often scattered across logs; this skill provides a quick way to summarize per-model usage costs from local cost JSON, including the current-most expensive model or a full breakdown by model.
Run the included scripts/model_usage.py to summarize per-model costs from the local codexbar cost logs for Codex or Claude.
High-intent search queries and answers about installing and using this skill.
To summarize per-model usage costs, you can run a script that extracts and aggregates totals for Codex and Claude models directly from your local CodexBar cost JSON logs, providing a quick breakdown without online access.
Comparing Codex and Claude model costs locally involves parsing the JSON output from the codexbar cost command to generate a full breakdown of total expenses per model, enabling data-driven, cost-aware model selection.
Yes, summarizing model usage costs requires the CodexBar CLI to be installed, as the skill specifically reads and processes the JSON input data generated by the codexbar cost command on your local machine.
Yes, you can get a cost breakdown for just the current top model, as the skill offers options to either report only the single most expensive model or provide a full breakdown of all models from the daily cost entries.
Local cost summarization needs JSON input from codexbar cost because the skill is designed to parse this specific structured data format to accurately extract and aggregate daily usage expenses for each AI model.
When extracting per-model usage costs, the available output formats are text or JSON, allowing you to either read a quick human-readable summary or pass the structured cost data downstream to other applications.
eggent-aiSummarize CodexBar local model usage costs by model from JSON input.
WellFitCommunityMonitor AI spending and usage across MCP and automation skills.
borealBytesParse CodexBar JSON logs to aggregate per-model cost breakdowns.
AlbertLin821Analyze Claude Code JSONL session logs to calculate token usage and cost savings.
