prompt-lookup

The prompt-lookup skill serves as a gateway to the prompts.chat repository, a vast, curated collection of optimized prompts designed for diverse AI models including ChatGPT, Claude, Gemini, and others. It is designed for developers, researchers, content creators, and AI enthusiasts who need to bridge the gap between intent and effective AI execution. By leveraging the power of an Model Context Protocol (MCP) server, this skill allows users to perform semantic searches, retrieve structured prompt templates, and utilize built-in enhancement tools to refine their inputs for better model performance. It removes the guesswork from prompting by providing standardized, high-quality, and community-vetted templates categorized by domain.

• Perform granular searches using keywords, categories, or specific tags such as coding, writing, or academic research.

• Seamlessly retrieve pre-configured prompt templates with support for dynamic variable injection (e.g., ${variable:default}).

• Utilize automated prompt improvement tools that offer output type and format control, including structured JSON or YAML results.

• Access a wide variety of prompt types including TEXT, STRUCTURED, IMAGE, VIDEO, and AUDIO contexts.

• Facilitate continuous learning and prompt engineering improvement through advanced iteration features.

• Users should always initiate a search before drafting custom prompts to leverage existing community knowledge.

• When prompted with variables, ensure all required fields are filled; variables with defaults are optional and can be skipped.

• The tool provides detailed metadata for each prompt, including author information, category tags, and direct links to the source library.

• When requesting prompt improvements, specify the desired output format (text, json, yaml) to ensure the returned content aligns with your technical pipeline or application requirements.

• Use this skill to explore advanced prompting strategies such as chain-of-thought reasoning, few-shot learning, and task-specific role-playing, which are documented extensively in the associated prompt library.