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
Installs just this skill. Get the whole plugin for auto-invocation.
⚡ How it fires
How this skill gets triggered: by you, by Claude, or both.
Fires itselfClaude auto-loads it when your prompt matches the work.
You can call itInvoke it directly when you want it.
Slash command/prompt-engineering-patterns
👁️ Context preview
The summary Claude sees to decide when to auto-load this skill.
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
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Stars38,174
Forks4,092
LanguagePython
LicenseMIT
📦 Ships with wshobson-agents
</> SKILL.md
prompt-engineering-patterns.SKILL.md
---name: prompt-engineering-patterns
description: >-
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.
---# 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
## Core Capabilities
### 1. Few-Shot Learning
- Example selection strategies (semantic similarity, diversity sampling)
- Balancing example count with context window constraints
