Enhance and standardize prompts for Large Language Models (LLMs) through composable decorators with this framework.
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Prompt Decorators is a framework designed to standardize and enhance Large Language Model (LLM) prompts using composable decorators. Featuring an official specification and Python reference implementation, it allows users to modify LLM behavior through simple annotations, promoting consistency and reusability across different platforms. By offering a systematic approach to prompt engineering, this framework addresses the challenges of inconsistency, verbosity, and cognitive overhead, making prompt crafting more modular and maintainable, while also integrating with tools like Claude Desktop through its MCP server integration.
Key Features
01Parameter validation and type checking
02Automatic documentation generation
03Registry-based decorator management
04Comprehensive library of 140+ pre-built decorators
05MCP Server integration for desktop AI applications
0614 GitHub stars
Use Cases
01Crafting prompts for specific reasoning patterns
02Structuring outputs in particular formats
03Ensuring consistent responses across different models
