prompt-decorators
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How to install prompt-decorators?
synaptiai/prompt-decorators
$git clone https://github.com/synaptiai/prompt-decorators**Prompt Decorators is a comprehensive framework that standardizes how prompts for Large Language Models (LLMs) are enhanced, structured, and transformed. This repository contains both the official Prompt Decorators Specification and its complete Python reference implementation.*
README.md
| 1 | # Prompt Decorators |
| 2 | |
| 3 | <div align="center"> |
| 4 | |
| 5 | <img src="https://synaptiai.github.io/prompt-decorators/assets/logo.png" alt="Prompt Decorators Logo" width="400"/> |
| 6 | |
| 7 | [](LICENSE) |
| 8 | [](https://www.python.org/) |
| 9 | [](https://synaptiai.github.io/prompt-decorators/) |
| 10 | [](https://github.com/astral-sh/ruff) |
| 11 | |
| 12 | [](https://github.com/synaptiai/prompt-decorators/actions?query=workflow%3A%22Code+Quality+and+Testing%22) |
| 13 | [](https://github.com/synaptiai/prompt-decorators/actions?query=workflow%3A%22Documentation%22) |
| 14 | [](https://github.com/synaptiai/prompt-decorators/actions?query=workflow%3A%22Publish+to+PyPI%22) |
| 15 | |
| 16 | **Prompt Decorators is a comprehensive framework that standardizes how prompts for Large Language Models (LLMs) are enhanced, structured, and transformed. This repository contains both the official Prompt Decorators Specification and its complete Python reference implementation.** |
| 17 | |
| 18 | [Documentation](https://synaptiai.github.io/prompt-decorators/) • |
| 19 | [Prompt Decorators Specification](https://synaptiai.github.io/prompt-decorators/prompt-decorators-specification-v1.0/) |
| 20 | |
| 21 | </div> |
| 22 | |
| 23 | ## 📋 Table of Contents |
| 24 | |
| 25 | - [Overview](#overview) |
| 26 | - [What Are Prompt Decorators?](#what-are-prompt-decorators) |
| 27 | - [Key Components](#key-components) |
| 28 | - [Background & Motivation](#background--motivation) |
| 29 | - [Challenges in Prompt Engineering](#challenges-in-prompt-engineering) |
| 30 | - [Benefits of Prompt Decorators](#benefits-of-prompt-decorators) |
| 31 | - [Key Features](#key-features) |
| 32 | - [Implementation Status](#implementation-status) |
| 33 | - [Getting Started](#getting-started) |
| 34 | - [Installation](#installation) |
| 35 | - [Basic Usage](#basic-usage) |
| 36 | - [License](#license) |
| 37 | - [Contributing](#contributing) |
| 38 | - [Acknowledgments](#acknowledgments) |
| 39 | |
| 40 | ## 🔍 Overview |
| 41 | |
| 42 | ### What Are Prompt Decorators? |
| 43 | |
| 44 | Prompt Decorators introduces a standardized annotation system inspired by software design patterns that allows users to modify LLM behavior through simple, composable "decorators." By prefixing prompts with annotations like `+++Reasoning`, `+++StepByStep`, or `+++OutputFormat`, users can consistently control how AI models process and respond to their requests across different platforms and implementations. |
| 45 | |
| 46 | This project addresses the growing complexity of AI interactions by providing: |
| 47 | |
| 48 | 1. **The Specification**: A formal standard that defines decorator syntax, behavior, and extension mechanisms |
| 49 | 2. **The Python Implementation**: A production-ready reference implementation with comprehensive tooling |
| 50 | 3. **MCP Integration**: A Model Context Protocol server that enables prompt decorator functionality in tools like Claude Desktop |
| 51 | |
| 52 | ### Key Components |
| 53 | |
| 54 | - **📝 Specification**: The formal [Prompt Decorators Specification (v1.0)](https://synaptiai.github.io/prompt-decorators/prompt-decorators-specification-v1.0/) defining the standard |
| 55 | - **🛠️ Core Framework**: A Python implementation with registry-based decorator management |
| 56 | - **🧩 140+ Decorators**: A comprehensive library of pre-built decorators covering reasoning, formatting, and more |
| 57 | - **🔌 MCP Server**: Integration with the Model Context Protocol for use with desktop AI applications |
| 58 | - **🧷 Claude Code Plugin**: A drop-in [Claude Code plugin](./claude-code-plugin/) that expands inline `::Name(params)` sigils into precise instructions before the model sees the prompt. Ships with a `/decorate` slash command, always-on config, and an opt-in Haiku-powered auto-selector. |
| 59 | - **📚 Extensive Documen |
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