Refine vague prompts into precise, model-specific prompts with explicit goals and output structure.
npx skills add https://github.com/riez/dots --skill prompt-crafting
Please help me install this Agent Skill. Skill: prompt-crafting Source: https://github.com/riez/dots/tree/main/.config/agentic/skills/prompt-crafting Command: npx skills add https://github.com/riez/dots --skill prompt-crafting
Refine vague prompts into precise, model-specific prompts. This guide helps convert underspecified requests into clear, outcome-oriented instructions that align with the target model family.
Refine a rough prompt into a model-specific prompt with explicit sections and a clear output format.
High-intent search queries and answers about installing and using this skill.
To refine a vague prompt, you convert underspecified requests into clear instructions by defining explicit goals, context, constraints, acceptance criteria, and a structured output format tailored to the target model family.
Structuring prompts for GPT, Claude, and Gemini requires applying model-specific refinement tips to ensure consistent results across families, focusing on explicit goals and a clearly defined output shape for each model.
You need prompt engineering for complex tasks when initial requests are underspecified, aiming to reduce misinterpretation and improve output quality by enforcing explicit context, constraints, and success criteria in the instructions.
You add explicit constraints and success criteria by transforming an underspecified request into an outcome-oriented instruction, defining the exact boundaries, goals, and acceptance metrics required for the task to succeed.
Prompt crafting works for improving output consistency by applying model-specific refinement tips, ensuring that instructions align with the target families of GPT, Claude, and Gemini to reduce misinterpretation.
AI models misinterpret complex tasks due to underspecified requests; fixing this requires converting vague inputs into precise prompts that enforce explicit goals, context, constraints, and a defined output structure.
POWERFULMOVESAsk targeted questions to clarify underspecified user requests before task execution.
hkcm91Refine vague user prompts into context-rich StickerNest development requests.
RightNow-AIDesign prompts for chain-of-thought, few-shot learning, and structured output.
drbobberAnalyze and refine AI prompts with advanced techniques like Chain-of-Thought.
