Merge a base command template with user-provided task details into a standalone prompt.
npx skills add https://github.com/nashgao/mqtt-client --skill prompt-nashgao
Please help me install this Agent Skill. Skill: prompt Source: https://github.com/nashgao/mqtt-client/tree/main/.claude/skills/prompt Command: npx skills add https://github.com/nashgao/mqtt-client --skill prompt-nashgao
This Skill streamlines prompt engineering by automatically combining a base command template with specific user task details, ensuring complete and contextually relevant prompts.
next.md template with user-provided arguments.next.md template to produce a ready-to-use, comprehensive prompt.Use the prompt skill to synthesize a prompt for creating a Go API with JWT authentication.
High-intent search queries and answers about installing and using this skill.
To synthesize an AI prompt, this tool merges a base command template with your specific task details. It ensures structural preservation and natural integration of arguments to produce a standalone, copy-pasteable output format.
Generating context-aware prompts for LLM automation involves adapting the prompt based on task specifics like technologies or complexity. This approach ensures complete and contextually relevant prompts by merging user-provided arguments with a template.
Yes, you can use a markdown template like next.md to create a ready-to-use AI prompt. The synthesis process combines this base command template with your provided arguments to output a comprehensive, standalone prompt.
To generate a detailed prompt for an AI to write a Python script, provide your specific task details to the synthesis tool. It will merge your request with a base template to produce a ready-to-use, comprehensive prompt.
Prompt engineering automation works with specific technologies like Go APIs by adapting the prompt based on task specifics. It takes your request and the base template to produce a ready-to-use, comprehensive prompt for your target technology.
A limitation of synthesizing prompts with a base template is the dependency on structural preservation of the original template. While it ensures natural integration of arguments, the output quality relies heavily on the base template's initial design.
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