model-usageUse CodexBar CLI local cost usage to summarize per-model usage for Codex or Claude, including the current (most recent) model or a full model breakdown. Trigger when asked for model-level usage/cost data from codexbar, or when you need a scriptable per-model summary from codexbar cost JSON.
Install via ClawdBot CLI:
clawdbot install steipete/model-usageInstall CodexBar (brew cask):
Install CodexBar (brew cask)Requires:
Get per-model usage cost from CodexBar's local cost logs. Supports "current model" (most recent daily entry) or "all models" summaries for Codex or Claude.
TODO: add Linux CLI support guidance once CodexBar CLI install path is documented for Linux.
1) Fetch cost JSON via CodexBar CLI or pass a JSON file.
2) Use the bundled script to summarize by model.
python {baseDir}/scripts/model_usage.py --provider codex --mode current
python {baseDir}/scripts/model_usage.py --provider codex --mode all
python {baseDir}/scripts/model_usage.py --provider claude --mode all --format json --prettymodelBreakdowns.modelsUsed when breakdowns are missing.--model when you need a specific model.codexbar cost --format json --provider .codexbar cost --provider codex --format json > /tmp/cost.json
python {baseDir}/scripts/model_usage.py --input /tmp/cost.json --mode all
cat /tmp/cost.json | python {baseDir}/scripts/model_usage.py --input - --mode current--format json --pretty).references/codexbar-cli.md for CLI flags and cost JSON fields.Generated Feb 15, 2026
Development teams using multiple AI models (Codex/Claude) track per-model spending to optimize their budget. The skill helps identify which models are driving costs during daily development cycles, enabling teams to adjust usage patterns or switch to more cost-effective models for specific tasks.
Freelancers using AI tools for client work need to attribute costs to specific projects. This skill allows them to generate per-model usage reports that can be included in client invoices, providing transparency about which AI services were utilized and their associated costs for each project.
Early-stage startups with limited budgets need to monitor AI spending closely. The skill helps founders track which models (Codex vs Claude) are being used most and at what cost, enabling data-driven decisions about which AI services to prioritize as they scale their operations.
Academic or corporate research labs testing multiple AI models need to compare not just performance but also costs. This skill provides structured cost breakdowns that researchers can use alongside performance metrics to determine the most cost-effective models for their specific research tasks.
Marketing or content agencies using AI for client deliverables need to allocate costs across departments. The skill helps managers understand which teams are using which models, enabling better resource planning and ensuring high-cost models are reserved for premium client work where they provide the most value.
Offer AI cost monitoring as a service to businesses using multiple AI models. Provide detailed breakdowns showing which models drive expenses, helping clients optimize their AI spending. Charge monthly subscription fees based on the number of models monitored and reporting frequency.
Consult with companies to analyze their AI usage patterns and recommend cost-saving strategies. Use the skill's data to identify expensive model usage and suggest alternatives or usage patterns that maintain quality while reducing costs. Charge project-based or retainer fees.
Build the skill into larger AI management platforms that handle multiple providers. Offer automated cost tracking alongside other features like performance monitoring and compliance checks. Generate revenue through platform licensing or enterprise contracts with larger organizations.
💬 Integration Tip
Install CodexBar CLI first via Homebrew, then ensure the Python script can access the cost JSON output. For automation, set up scheduled runs to generate daily usage reports.
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