Optimizes embedded LLM system prompts through iterative failure analysis and performance evaluation.
The Caro Prompt Tuner skill provides a structured, five-phase framework for improving the command generation accuracy of the Caro CLI's embedded backend. It guides developers through measuring baselines, analyzing failure patterns—such as macOS/BSD flag inconsistencies or pathing errors—and implementing targeted prompt improvements. By following this systematic workflow, users can move from low-accuracy baselines to high-performance command translation, ensuring natural language tasks are converted into safe, reliable POSIX commands for local execution.
Key Features
01Detailed failure pattern analysis for BSD/macOS vs. GNU utilities
02Systematic 5-phase tuning workflow for LLM prompts
03Standardized success metrics and benchmark tracking
0423 GitHub stars
05Automated accuracy measurement and performance verification
06Tailored optimization strategies for small, embedded models
Use Cases
01Fixing recurring edge cases in CLI automation tasks
02Reducing hallucinations in local LLM command generation
