Open-source local hook automatically switches Claude models to cut AI costs

A developer has open-sourced a local hook that automatically selects the most cost-effective Claude AI model based on the type of coding task, potentially reducing AI costs by 50-70% without quality loss.

How it works

The tool runs as a local hook in Cursor and Claude Code (both use the same hook system) before each prompt is sent. It sits next to Opus/plan and acts as an efficient front-end filter that prevents obviously bad model matches before they hit expensive models.

Key functionality

• Reads the prompt and current model selection
• Uses simple keyword rules to classify tasks (git operations, feature work, architecture/deep analysis)
• Blocks if you're overpaying (e.g., Opus for git commit) and suggests Haiku or Sonnet
• Blocks if you're underpowered (Sonnet/Haiku for architecture) and suggests Opus
• Lets everything else through unchanged
• ! prefix bypasses the filter completely if you disagree with its suggestion

Technical details

• 3 files: bash + python3 + JSON
• No proxy, no API calls, no external services
• Fail-open design: if it hangs, Claude Code proceeds normally
• Open-sourced at: https://github.com/coyvalyss1/model-matchmaker

Performance and testing

The developer analyzed several weeks of their own prompts and found:

• 60-70% were standard feature work Sonnet could handle
• 5-20% were debugging/troubleshooting
• A significant portion were pure git/rename/formatting tasks that Haiku handles identically at 90% less cost

Retroactive analysis showed the tool would have cut 50-70% of AI spend with no quality drop. After tuning, it correctly handled 12/12 real test prompts.

Problem it solves

The issue isn't knowledge—developers know they should switch models—but friction. When in flow state, developers don't want to think about dropdown menus. This tool automates the decision-making process.