This repository contains a collection of advanced prompts designed to leverage large language models (LLMs) for specific academic and professional tasks. The prompts aim to guide users through complex processes like writing scientific articles, planning educational content, auditing contracts, and exploring scientific concepts, acting as specialized AI assistants.

How It Works

The core approach involves structuring detailed, multi-turn conversational prompts that guide both the user and the LLM through a series of steps. Each prompt defines a persona (e.g., academic expert, legal advisor, educational planner) and outlines a workflow with specific commands and expected outputs. This structured interaction aims to elicit precise, context-aware responses, enhancing the LLM's utility for specialized tasks.

Quick Start & Requirements

• Primary install / run command: No specific installation is required beyond access to a compatible LLM (e.g., ChatGPT). Users copy and paste the prompt text into their LLM interface.
• Non-default prerequisites and dependencies: Access to a capable LLM.
• Links: None provided.

Highlighted Details

• Multidisciplinary Focus: Prompts cover a wide range of fields including linguistics, education (BNCC alignment), law, quantum mechanics, business simulation, and blockchain development.
• Structured Workflows: Each prompt provides a step-by-step guide with defined commands (e.g., /definir_tema, /clause_analysis) for specific tasks.
• Persona-Based Interaction: Prompts often define specific AI personas (e.g., Dr. Linguo-Researcher, Dr. Alex Quanta) to tailor the interaction style and expertise.
• Adaptive Menus & Feedback: Several prompts incorporate adaptive menus and sentiment analysis to personalize the user experience and refine responses.

Maintenance & Community

• No information on contributors, community channels, or roadmap is available in the provided README.

Licensing & Compatibility

• No license information is provided.

Limitations & Caveats

The repository consists solely of prompt text; it does not contain executable code or a framework for running these prompts. The effectiveness of these prompts is entirely dependent on the capabilities and implementation of the underlying LLM.