What problem does it solve?
Users of AI coding tools like Claude and GPT often lack visibility into how much their AI-assisted development costs per feature, leading to unoptimized spending, unclear ROI, and unexpected budget overruns.
Core Features & Use Cases
- Per-Feature Cost Tracking: Log and track AI token costs for individual features, bug fixes, and refactoring tasks to identify high-spend work.
- Interactive Cost Dashboard: View breakdowns of total spend, weekly/monthly trends, cost per model, and outlier tasks that cost 3x the average.
- Actionable Optimization Recommendations: Get tailored suggestions to reduce costs, such as switching to cheaper models for routine tasks or investing in tests to cut debugging expenses.
- Build vs Buy ROI Analysis: Evaluate whether building a feature with AI is more cost-effective than using existing libraries or SaaS tools, with clear cost benchmarks for common development tasks.
- Use Case: A solo founder building a SaaS product can use this skill to track that their authentication feature cost $12 in AI tokens, identify that debugging accounts for 40% of their spend, and get a recommendation to add unit tests to reduce future debugging costs.
Quick Start
Use the cost skill to analyze your current AI spending for this project and identify the top 3 ways to reduce unnecessary costs.