| 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
