prompt-lookup

The prompt-lookup skill connects your development environment to the prompts.chat repository, allowing direct access to a vast collection of curated AI templates. By acting as an interface between your local tools and the MCP server, this skill allows you to search, retrieve, and refine prompts without manual browsing. It specifically addresses the friction of crafting effective system instructions by offering a searchable index and automated enhancement tools. Whether you are generating code, seeking creative writing assistance, or configuring complex agents, this utility retrieves formatted prompt templates tailored to your specific needs. It manages variable substitution and structure requirements, ensuring that the prompts you import are ready for immediate deployment in your AI workflows while maintaining consistency across your various AI-driven tasks and projects.

When to Use This Skill

• •Finding specialized prompts for complex code review or refactoring tasks
• •Optimizing a draft prompt for better output performance or structural integrity
• •Retrieving pre-built structured prompts for image generation or data analysis
• •Managing prompt library discovery directly within your terminal or editor

How to Invoke This Skill

Example prompts that trigger this skill in Claude Code, Cursor, or Antigravity:

• “Find a prompt for writing unit tests
• “Get prompt template for summarizing technical documents
• “Improve this draft prompt to be more descriptive
• “Search the prompt library for Python coding assistance
• “Show me available writing prompts in the coding category

Pro Tips

• 💡When searching, be as specific as possible with your keywords to narrow down results. For example, 'Python code review best practices prompt' is better than just 'code review prompt'.
• 💡After retrieving a prompt, use the `improve_prompt` tool to tailor it to your exact needs, adding specific context or constraints for superior results.
• 💡Familiarize yourself with different prompt types (TEXT, STRUCTURED, IMAGE) and categories to efficiently filter searches and discover specialized prompts.

What this skill does

• •Keyword-based searching of the prompts.chat repository
• •Direct retrieval of specific prompt templates via unique identifiers
• •Automated variable injection and handling for parameterized prompts
• •AI-assisted refinement and formatting of raw prompt text
• •Filtering of search results by media type, category, or classification tag

When not to use it

• ✕When you need to create custom logic that is highly specific to a proprietary, non-public codebase
• ✕When the task does not involve interacting with an LLM or generative AI model

Example workflow

1. The user asks for a prompt to handle technical documentation.
2. The skill executes search_prompts with the keyword 'documentation'.
3. The user reviews the results and identifies a suitable prompt ID.
4. The skill calls get_prompt to retrieve the specific content.
5. The system detects variables and requests input values from the user.
6. The user confirms the finalized text for immediate use.

Prerequisites

• –Configured prompts.chat MCP server
• –Active internet connection for repository access

Pitfalls & limitations

• !Failing to supply values for required prompt variables will result in errors.
• !Over-improving a prompt can sometimes strip away the original, intentional style.
• !Search results are limited to the content currently available in the prompts.chat index.

FAQ

How it compares

Unlike manual searching in a browser, this skill brings the prompt content directly into your development workflow and automates the variable-filling process.

Source & trust

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