prompt-engineer

Design and optimize AI prompts with cost-aware evaluation for LLM applications.

Updated Apr 10, 2026

Search Tags:#versioning #prompt engineering #ai #llm #evaluation #cost optimization

Ready to run

npx skills add https://github.com/Exia-thd/Digital-Nervous --skill prompt-engineer-exia-thd

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: prompt-engineer
Source: https://github.com/Exia-thd/Digital-Nervous/tree/main/skills/prompt-engineer
Command: npx skills add https://github.com/Exia-thd/Digital-Nervous --skill prompt-engineer-exia-thd

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It provides a production-grade pipeline for designing, optimizing, and evaluating AI prompts—including system prompts, chain‑of‑thought, few‑shot examples, versioning, and cost optimization—so teams can deliver reliable LLM‑powered features.

Core Features & Use Cases

End‑to‑end workflow covering task analysis, prompt architecture, automated evaluation, cost optimization, and production hardening.
Conditional activation based on AI feature requests, existing LLM calls, or explicit user prompts.
Brownfield awareness with prompt style matching, versioning, and A/B testing for existing codebases.
Scalable cost modeling at 1×, 10×, and 100× request volumes.

Quick Start

Ask the Prompt Engineer to create a production‑ready prompt for a customer support chatbot using GPT‑4.

Frequently Asked Questions about prompt-engineer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I optimize AI prompts for production LLM applications?▼

To optimize AI prompts for production LLM applications, apply an end-to-end pipeline covering task analysis, prompt architecture, automated evaluation, and cost optimization. This workflow ensures reliable LLM-powered features by structuring system prompts and few-shot examples.

What is the best way to reduce LLM API costs without losing quality?▼

The best way to reduce LLM API costs without losing quality is to apply scalable cost modeling at 1×, 10×, and 100× request volumes. This cost-aware evaluation balances prompt architecture and chain-of-thought depth to maintain output fidelity.

How do I set up A/B testing for prompt versioning in an existing codebase?▼

To set up A/B testing for prompt versioning in an existing codebase, apply brownfield awareness to match existing prompt styles. This enables structured versioning and automated evaluation to compare variations against target model specifications.

What do I need to provide to design a system prompt for a specific target model?▼

To design a system prompt for a specific target model, you need to provide detailed feature requirements, the target model specification, and optional existing prompts. These inputs drive task analysis and prompt architecture for production-grade applications.

Does this prompt engineering workflow support chain-of-thought and few-shot examples?▼

Yes, this prompt engineering workflow supports chain-of-thought and few-shot examples. It integrates these techniques into the prompt architecture during task analysis to improve LLM reasoning and output accuracy for production features.

When should I use automated evaluation for AI prompts?▼

You should use automated evaluation for AI prompts when hardening LLM features for production. It validates prompt variations during A/B testing and versioning, ensuring the chain-of-thought and few-shot examples perform reliably under scaled request volumes.