domains / families / agent-tooling / prompt-engineering-patterns

prompt-engineering-patterns

combined

Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, or designing production prompt templates. Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability.

When to use

• 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
• Using structured outputs (JSON mode) for reliable parsing

When not to use

• The task is unrelated to prompt engineering patterns
• You need a different domain or tool outside this scope
• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

Skill page

Conflict

The first two copies are near-identical, but the third copy (LeoYeAI/openclaw-master-skills) diverges significantly. It replaces the generic 'prompt_optimizer' library with specific LangChain and Anthropic SDK code, adds a 'Structured Outputs' capability section (Pydantic/JSON mode) that the others lack, and provides different key patterns (e.g., Pydantic schema enforcement, SemanticSimilarityExampleSelector). Because the third copy contains material steps and constraints (specific library usage, structured output patterns) that the others do not, and the others contain a different 'Quick Start' implementation, crowning one would hide a real alternative implementation approach.