Design optimal prompts and task breakdowns for complex AI reasoning.
npx skills add https://github.com/lshtram/core_dev --skill meta-prompting
Please help me install this Agent Skill. Skill: meta-prompting Source: https://github.com/lshtram/core_dev/tree/main/.agent/skills/meta-prompting Command: npx skills add https://github.com/lshtram/core_dev --skill meta-prompting
meta-prompting helps users design and refine prompts to reliably guide AI reasoning, breaking complex tasks into actionable steps.
Decompose a complex task into atomic steps and generate a reusable prompting scaffold for each step.
High-intent search queries and answers about installing and using this skill.
To design prompts that guide AI reasoning, you decompose complex requests into actionable atomic steps. This ensures the AI executes multi-step planning and analysis reliably through structured instructions.
Chain-of-thought prompt engineering breaks complex reasoning into sequential atomic steps. Use it for multi-step analysis and complex planning to improve AI task execution accuracy.
Yes, tree-of-thought techniques decompose complex requests into atomic steps for multi-step analysis and planning. This creates reusable reasoning scaffolds that guide AI thinking across cross-domain tasks.
The best way to create reusable prompts is applying recursive refinement to build reasoning scaffolds. This generates structured prompting frameworks adaptable for complex cross-domain task execution.
Break down a complex request by applying prompt engineering techniques like chain-of-thought and tree-of-thought. This decomposes tasks into atomic steps, creating a reusable scaffold for each instruction.
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