When should I run a prompt tuning cycle?

You should use this skill whenever Caro's command generation accuracy is low, after adding new test cases, or when the model consistently fails on specific edge cases or macOS-specific flags.

What metrics are used to define success?

Success is measured by running internal evaluation tests and aiming for an accuracy score of 85% or higher, with specific focus on eliminating semantic errors in command generation.

What is the primary purpose of the Prompt Tuner skill?

The Prompt Tuner skill provides a guided workflow to iteratively improve the system prompt of the Caro CLI's embedded model, increasing its ability to generate accurate POSIX commands.

How does the skill handle macOS vs. Linux differences?

The workflow includes specific analysis patterns for BSD vs. GNU flags, ensuring that the prompt is tuned to generate commands compatible with the macOS environment.

Caro Prompt Tuner

Name: Caro Prompt Tuner
Author: wildcard

bywildcard

•

数据科学与机器学习

Optimizes embedded LLM system prompts through iterative failure analysis and performance evaluation.

关于

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.

主要功能

Detailed failure pattern analysis for BSD/macOS vs. GNU utilities
Systematic 5-phase tuning workflow for LLM prompts
Standardized success metrics and benchmark tracking
23 GitHub stars
Automated accuracy measurement and performance verification
Tailored optimization strategies for small, embedded models

使用场景

Fixing recurring edge cases in CLI automation tasks
Reducing hallucinations in local LLM command generation
Improving natural-language-to-POSIX command accuracy

关于

主要功能

Detailed failure pattern analysis for BSD/macOS vs. GNU utilities
Systematic 5-phase tuning workflow for LLM prompts
Standardized success metrics and benchmark tracking
23 GitHub stars
Automated accuracy measurement and performance verification
Tailored optimization strategies for small, embedded models

使用场景

Fixing recurring edge cases in CLI automation tasks
Reducing hallucinations in local LLM command generation
Improving natural-language-to-POSIX command accuracy