prompt-engineering-patterns — Skills

What it does

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

Advanced prompt engineering techniques for optimizing LLM performance, reliability, and structured outputs in production.

Source

Repository: https://github.com/wshobson/agents

prompt-engineering-patterns FAQ

What does the prompt-engineering-patterns skill do?

>- 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. Designing complex prompts for production LLM applications Optimizing prompt performance and consistency

What does prompt-engineering-patterns require?

Reducing token usage while maintaining quality Context overflow: Exceeding token limits with excessive examples Token Usage: Average tokens per request

How do I install prompt-engineering-patterns?

Run: npx -y skills add https://github.com/wshobson/agents --skill prompt-engineering-patterns --agent claude-code — the source lives at github.com/wshobson/agents.

Maintain prompt-engineering-patterns?

Let people know it's listed here — add the badge (live metrics, light/dark aware) or a plain link to your README or docs.

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npx agentictools info skills/wshobson-agents-prompt-engineering-patterns

The second line is the CLI lookup for this page — handy in READMEs and docs.