prompt-lookup

This skill acts as a comprehensive interface for the world's largest open-source prompt library, enabling users to efficiently retrieve, refine, and execute AI prompts. It is designed for developers, students, content creators, and AI enthusiasts who require high-quality, battle-tested prompt templates to improve their interactions with LLMs like GPT-4, Claude, and Gemini. By integrating with the prompts.chat MCP server, this skill allows for programmatic searching across vast categories, facilitating faster workflows and more reliable prompt engineering outcomes.

• Perform deep semantic searches to find relevant prompt templates based on specific keywords, categories, or tags.

• Retrieve structured prompt data, including author information, descriptions, and metadata, using unique prompt IDs.

• Utilize intelligent prompt refinement to automatically optimize existing text, structure, or code prompts for better performance and output accuracy.

• Support dynamic variable injection within templates, allowing users to customize prompts with specific contextual inputs like topics, personas, or constraints.

• Seamlessly integrate with various AI platforms and development environments, supporting multiple output formats including raw text, JSON, and YAML.

• Start by using the search tool to explore categories like coding, writing, or analysis before drafting your own custom prompts to ensure you are leveraging existing community knowledge.

• When retrieving prompts with dynamic variables, ensure the provided inputs align with the expected structure to prevent execution errors.

• The improvement tool should be used iteratively; start with a basic prompt, refine it for your specific output requirements, and then save the enhanced version for recurring tasks.

• Be mindful of output type requirements; specify whether you are targeting image, video, or audio generation if the task deviates from standard text-based interaction.

• This tool is best used in tandem with IDEs or AI-native coding assistants, where prompts can be directly applied to code reviews, documentation generation, or logical debugging tasks.