Structure prompts with few-shot learning and system prompt architecture.
npx skills add https://github.com/0xRayAI/xray --skill prompt-engineering-0xrayai
Please help me install this Agent Skill. Skill: prompt-engineering Source: https://github.com/0xRayAI/xray/tree/main/integrations/prompt-engineering Command: npx skills add https://github.com/0xRayAI/xray --skill prompt-engineering-0xrayai
This skill addresses the inconsistency and sub-optimal performance often found in raw LLM interactions by providing structured, proven frameworks for prompt design.
Apply the prompt engineering skill to analyze my current system prompt and suggest three specific improvements for better output consistency.
High-intent search queries and answers about installing and using this skill.
To fix inconsistent LLM outputs, apply prompt engineering patterns like Chain-of-Thought reasoning to force the model to verify its logic before generating the final result, improving accuracy and output consistency.
The best way to structure system prompts is by using established prompt hierarchies and system prompt architecture, which ensures consistent model performance and reliable agent behavior across diverse agentic workflows.
Few-shot learning improves LLM optimization by providing structured examples within the prompt, which guides the model's reasoning process and significantly enhances accuracy for complex task decomposition.
Yes, you can use prompt engineering to reduce token waste by applying systematic testing and iterative refinement strategies to debug agent behavior and optimize overall LLM interaction quality.
Poor LLM performance on complex tasks often occurs because raw interactions lack structured frameworks, which can be resolved by implementing complex task decomposition and iterative refinement techniques.
Yes, you need adherence to established evaluation metrics and prompt hierarchies during systematic testing to ensure consistent model performance and reliable agent behavior throughout diverse workflows.
hitsumabushi334Improves LLM prompt reliability through few-shot learning, chain-of-thought prompting, and template systems for agents and sub-agents.
z1439527767Analyze AI agent performance and apply systematic optimization workflows.
fakhriaditiarahmanOptimize and evaluate LLM prompts using Chain-of-Thought, ReAct, and Few-Shot learning.
89jobrienOptimize LLM prompts with structured design, few-shot patterns, and chain-of-thought reasoning.
