What is the primary benefit of the AI Prompt Engineer skill?

It reduces the amount of information a user needs to provide by allowing the AI to intelligently infer context from the environment, file types, and session history.

How does this skill handle different operating systems?

It uses platform-aware routing to ensure generated commands use the correct syntax and tools for the specific environment, such as macOS (BSD) versus Linux (GNU).

What is 'The File Type Principle' in this skill?

It is a logic framework that treats file extensions as the strongest signal for tool selection, ensuring the AI automatically chooses the right utility, like 'unzip' for .zip files on macOS.

Can this skill optimize prompts for specific models?

Yes, it includes templates and logic to tailor prompts for different model sizes and architectures, ensuring optimal performance for both local small models and large cloud LLMs.

AI Prompt Engineer

Name: AI Prompt Engineer
Author: wildcard

bywildcard

•

데이터 과학 및 ML

Optimizes prompt strategies and context inference to transform natural language into precise, platform-aware POSIX commands with minimal user input.

The AI Prompt Engineer skill provides a structured framework for enhancing agentic behavior within the Caro CLI environment. It addresses the challenge of user friction by implementing intelligent context inference from file types, platform specifics, and session history. By leveraging query taxonomy and tool routing logic, this skill ensures Claude can accurately map intent to the correct commands—such as distinguishing between BSD and GNU flags—while optimizing prompt templates for various model architectures like SmolLM or vLLM instances.

주요 기능

01Systematic Evaluation Framework for measuring inference accuracy

02Automated Platform-Aware Tool Routing for macOS and Linux

03Intelligent Query Taxonomy for intent categorization

04Multi-layered Context Inference (CWD, platform, file extensions)

05Model-Specific Prompt Optimization for various LLM sizes

0626 GitHub stars

사용 사례

01Optimizing agentic loops to route simple exploration tasks to faster, smaller local models.

02Reducing manual input by inferring project types from root markers like Cargo.toml or package.json.

03Developing a CLI that automatically selects the correct extraction tool based on file extension and OS.

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add wildcard/caro ai-prompt-engineer

For use in Claude.ai and ChatGPT

Download Skill