Design, test, and refine prompts for LLM applications with evaluation frameworks.
npx skills add https://github.com/Serg28/demosite --skill prompt-engineer-serg28
Please help me install this Agent Skill. Skill: prompt-engineer Source: https://github.com/Serg28/demosite/tree/main/.agents/skills/prompt-engineer Command: npx skills add https://github.com/Serg28/demosite --skill prompt-engineer-serg28
This Skill helps users design, refine, and evaluate prompts to improve large language model outputs with clarity and efficiency.
Use the prompt-engineer to craft a specific prompt for classifying customer feedback into positive or negative sentiment.
High-intent search queries and answers about installing and using this skill.
To optimize prompts for LLM output accuracy and token efficiency, you can use a systematic workflow to iteratively test and refine your prompt designs using diverse inputs and evaluation frameworks.
The best way to evaluate prompt performance is through systematic testing with diverse inputs, assessing the results via guided evaluation frameworks to measure clarity, correctness, and efficiency.
Yes, you can design prompts for few-shot learning, chain-of-thought reasoning, and structured outputs by applying systematic design and refinement workflows tailored for diverse LLM applications.
No specific external frameworks or dependencies are required to refine LLM prompts, as the skill operates independently using built-in scripts and reference materials to guide the optimization process.
Your document summarization prompt likely produces inconsistent results due to a lack of systematic testing and refinement, which can be resolved by iteratively evaluating diverse inputs to improve clarity and correctness.
You should use automated prompt evaluation when you need to rapidly scale testing across diverse inputs, though human assessment remains valuable for verifying nuanced clarity and correctness during the optimization workflow.
ContextJet-aiWrite evaluations before changing LLM prompts or models.
saffron-healthGuide writing effective system prompts for LLM agents.
AndyAnh174Evaluates LLM-powered systems using automated metrics, human feedback, and benchmarking.
take566Manages the end-to-end lifecycle of LLM applications, including prompt engineering, RAG, evaluation, and deployment.
