Optimize Claude Code CLI, Gemini, and Codex usage by analyzing metadata in logs. Reduces costs and frustration without affecting prompt quality. Ideal for operations teams managing AI workflows.
git clone https://github.com/netra-systems/zen.gitOptimize Claude Code CLI, Gemini, and Codex usage by analyzing metadata in logs. Reduces costs and frustration without affecting prompt quality. Ideal for operations teams managing AI workflows.
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/netra-systems/zenCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Analyze my Claude Code CLI, Gemini, and Codex usage logs to identify optimization opportunities. Focus on reducing costs while maintaining prompt quality. Here are the logs: [LOGS]. Provide specific recommendations for [COMPANY] in the [INDUSTRY] sector.
# AI Usage Optimization Report ## Cost Reduction Opportunities - **Redundant API Calls**: 30% of Codex calls could be eliminated by caching frequent queries. - **Model Selection**: 25% of Claude Code CLI calls could use cheaper models without quality loss. - **Batch Processing**: Gemini calls could be batched, reducing costs by 15%. ## Implementation Recommendations 1. **Implement Caching**: Store frequent Codex queries to reduce API calls. 2. **Model Tiering**: Use Claude Code CLI's cheaper models for non-critical tasks. 3. **Batch Processing**: Group Gemini calls to optimize usage. ## Estimated Savings - **Monthly Cost Reduction**: $1,200 - **Annual Cost Reduction**: $14,400
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