Design, optimize, and evaluate LLM prompts with structured workflows.
npx skills add https://github.com/scanady/nexus-agents --skill prompt-engineer-scanady
Please help me install this Agent Skill. Skill: prompt-engineer Source: https://github.com/scanady/nexus-agents/tree/main/skills/prompt-engineer Command: npx skills add https://github.com/scanady/nexus-agents --skill prompt-engineer-scanady
Prompt engineering for LLMs to maximize performance, reliability, and reproducibility across tasks and models.
Provide a ready-to-run prompt design for a sample task, including a chain-of-thought example, few-shot guidance, and a basic evaluation checklist.
High-intent search queries and answers about installing and using this skill.
LLM prompt design applies advanced techniques like chain-of-thought, few-shot, and structured outputs to improve accuracy. It uses formal testing frameworks and guardrails to evaluate prompt performance, ensuring reliability and reproducibility across different tasks and model families.
Structured outputs and few-shot prompting are applied within a structured workflow to codify output formats and validation steps. This approach constrains the LLM to specific formats and provides examples to guide generation, improving control over the final results.
Chain-of-thought prompting improves LLM performance by guiding the model through intermediate reasoning steps before producing a final answer. This technique is integrated into the prompt design workflow to enhance accuracy and reliability on complex tasks.
Cross-model prompt auditing, iteration, and deployment support allows teams to evaluate prompts across multiple model families. This ensures that prompt designs maintain their reliability, reproducibility, and performance when applied to different LLMs.
Formal testing frameworks are used to design, run, and evaluate prompts with guardrails. They codify validation and testing steps to maximize performance and reliability, preventing regressions during prompt iteration and deployment across models.
optioniApply few-shot learning, chain-of-thought, and structured output techniques to optimize LLM prompts.
viraatdasMonitor live applications for console errors, performance regressions, and page failures.
thejordanleopoldCreate reusable prompt templates and evaluation workflows for LLM tasks.
take566Manages the end-to-end lifecycle of LLM applications, including prompt engineering, RAG, evaluation, and deployment.
