AI SummaryTransforms rough prompts into production-ready LLM prompts using advanced techniques like Chain-of-Thought and RAG optimization. Ideal for developers, AI engineers, and teams building LLM applications who need to craft effective prompts across Claude, GPT, Llama, and other models.
Install
Copy this and paste it into Claude Code, Cursor, or any AI assistant:
I want to install the "prompt-engineer" skill in my project. Please run this command in my terminal: # Install skill into the correct directory mkdir -p .claude/skills/prompt-engineer-skill && curl --retry 3 --retry-delay 2 --retry-all-errors -o .claude/skills/prompt-engineer-skill/SKILL.md "https://raw.githubusercontent.com/repo-phuocdt/prompt-engineer-skill/main/SKILL.md" Then restart Claude Code (or reload the window in Cursor) so the skill is picked up.
Description
Transform rough prompts/ideas into production-ready LLM prompts. Use when crafting, refining, or optimizing prompts for any AI model (Claude, GPT, Llama, etc.) with advanced techniques like CoT, constitutional AI, RAG optimization.
Prompt Engineer
Expert prompt engineering skill that transforms rough ideas into well-structured, production-ready prompts optimized for LLMs.
When to Activate
• User provides a rough prompt/idea and wants it refined • User asks to create/design/optimize a prompt for any LLM • User needs prompt architecture for agents, RAG, or multi-step workflows • User asks about prompting techniques or best practices
1. Analyze Input
Identify from user's request: • Target model (Claude, GPT, Llama, etc.) — default: Claude • Use case (agent system prompt, task prompt, RAG, chat, etc.) • Domain (technical, creative, business, etc.) • Constraints (token limits, output format, safety requirements)
2. Apply Techniques
Select appropriate techniques from references/techniques.md based on use case: • Complex reasoning → Chain-of-Thought, Tree-of-Thoughts • Safety-critical → Constitutional AI patterns • Data extraction → Structured output, JSON mode • Multi-step tasks → Prompt chaining, agent patterns • Knowledge-heavy → RAG optimization
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