Individual skill
prompt-engineering-patterns
This skill should be used when the user asks to "optimize a prompt", "improve prompt performance", "design a prompt template", "write better prompts", "debug prompt issues", "use chain-of-thought", "structured prompting", "few-shot prompting", or wants to apply advanced prompt engineering patterns for production LLM applications.
plugins/llm-application-dev/skills/prompt-engineering-patterns/SKILL.md
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- Last checked
- Nov 9, 2026
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- License
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SKILL.mdcore · 5,216 bytesSKILL.mdcore · 5,216 bytesassets/few-shot-examples.jsonasset · 4,174 bytesassets/prompt-template-library.mddoc · 3,291 bytesreferences/chain-of-thought.mddoc · 9,401 bytesreferences/details.mddoc · 9,195 bytesreferences/few-shot-learning.mddoc · 11,188 bytesreferences/prompt-optimization.mddoc · 12,792 bytesreferences/prompt-templates.mddoc · 11,409 bytesreferences/system-prompts.mddoc · 5,444 bytesscripts/optimize-prompt.pyruns · 9,508 bytes
Read excerpt
# Prompt Engineering Patterns
Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability.
## When to Use This Skill
- 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