prompt-lookup_skill

Overview

This skill helps you discover, retrieve, and improve AI prompts using the prompts.chat MCP server. It connects search, fetch, and enhancement tools so you can find community prompts, pull a specific prompt by ID, or get AI-assisted improvements quickly. Use it to browse categories, fill prompt variables, and export polished templates for your workflows.

How this skill works

The skill first searches the prompts.chat index by keywords, category, tag, type, and result limit to return relevant prompt entries with title, description, author, category, tags, and links. It can fetch a specific prompt by ID and detect variables that require user input. For improvement, it sends the prompt text to an enhancement endpoint and returns a revised prompt plus a short changelog explaining what was improved.

When to use it

• You need a ready-made prompt template (e.g., code review, blog outline, persona-based assistant).
• You want to search community prompts across categories, tags, and types.
• You have a prompt you want improved or reformatted for a specific model/output.
• You want to fetch a prompt by ID and fill required variables before running it.
• You mention prompts.chat or ask for prompt libraries and examples.

Best practices

• Always run a search first to reuse existing community prompts before drafting from scratch.
• Specify desired output type and format (text, structured JSON/YAML) when requesting improvements.
• Provide any required variable values when fetching a prompt to avoid follow-up questions.
• Ask for suggested categories and tags if you plan to save or share an improved prompt.
• Review the changelog returned after improvement to understand edits and adjust tone or constraints.

Example use cases

• Find high-quality prompts for generating API docs or technical explanations.
• Retrieve a marketing email prompt by ID and fill recipient variables before sending.
• Improve a draft prompt to be more explicit for GPT-4 or other specific LLMs.
• Search prompts tagged "code-review" to build a consistent engineering checklist.
• Convert a free-text prompt into structured JSON for automated pipelines.