prompt-engineer

name	prompt-engineer
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

Workflow

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

3. Craft the Prompt

Follow model-specific guidelines from references/model-optimization.md:

• Structure with clear sections (role, context, instructions, output format)
• Include examples where beneficial (few-shot)
• Add constraints and guardrails
• Optimize for token efficiency

4. Deliver Output

MANDATORY format — always include ALL sections:

The Prompt

Display complete prompt in a single copyable code block.

Implementation Notes

• Techniques used and rationale
• Model-specific optimizations
• Parameter recommendations (temperature, max_tokens)
• Expected behavior and output format

Testing & Evaluation

• 3-5 test cases to validate
• Edge cases and failure modes
• Optimization suggestions

Usage Guidelines

• When/how to use effectively
• Customization options
• Integration considerations

Key Principles

• Always show the complete prompt — never just describe it
• Token efficiency — concise but comprehensive
• Production-ready — reliable, safe, optimized
• Model-aware — tailor to target model's strengths
• Refer to references/techniques.md for advanced technique details
• Refer to references/model-specific-optimization-guide.md for model-specific guidance
• Refer to references/production-patterns-and-enterprise-templates.md for enterprise patterns