Prompt Engineering Patterns

Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability.

Do not use this skill when

• The task is unrelated to prompt engineering patterns
• You need a different domain or tool outside this scope

Instructions

• Clarify goals, constraints, and required inputs.
• Apply relevant best practices and validate outcomes.
• Provide actionable steps and verification.
• If detailed examples are required, open resources/implementation-playbook.md.

Use this skill when

• Designing complex prompts for production LLM applications
• Optimizing prompt performance and consistency
• Implementing structured reasoning patterns (chain-of-thought, tree-of-thought)
• Building few-shot learning systems with dynamic example selection
• Creating reusable prompt templates with variable interpolation
• Debugging and refining prompts that produce inconsistent outputs
• Implementing system prompts for specialized AI assistants

Core Capabilities

🧠 Knowledge Modules (Fractal Skills)

1. 1. Few-Shot Learning

2. 2. Chain-of-Thought Prompting

3. 3. Prompt Optimization

4. 4. Template Systems

5. 5. System Prompt Design

6. Progressive Disclosure

7. Instruction Hierarchy

8. Error Recovery

9. With RAG Systems

10. With Validation

11. Token Efficiency

12. Latency Reduction