Prompt Engineering Patterns
Leverage proven prompt engineering patterns from rmynhdaris to optimize AI model outputs for clarity, accuracy, and desired results.
Install on your platform
We auto-selected Claude Code based on this skill’s supported platforms.
Run in terminal (recommended)
claude mcp add rmyndharis-prompt-engineering-patterns npx -- -y @trustedskills/rmyndharis-prompt-engineering-patterns
Or manually add to ~/.claude/settings.json
{
"mcpServers": {
"rmyndharis-prompt-engineering-patterns": {
"command": "npx",
"args": [
"-y",
"@trustedskills/rmyndharis-prompt-engineering-patterns"
]
}
}
}Requires Claude Code (claude CLI). Run claude --version to verify your install.
About This Skill
What it does
This skill provides a collection of prompt engineering patterns to improve the quality and consistency of AI agent responses. It offers structured approaches for tasks like role-playing, chain-of-thought reasoning, and generating different creative text formats. The goal is to guide agents toward more reliable and predictable outputs by leveraging established prompting techniques.
When to use it
- Complex Reasoning: When you need an AI agent to break down a problem into smaller steps and explain its logic.
- Creative Content Generation: To ensure the AI generates content in a specific style, format (e.g., poem, code), or tone.
- Role-Playing Scenarios: When simulating conversations with characters requiring consistent personality traits and behaviors.
- Consistent Output Style: When you need to enforce a particular writing style across multiple generations from the AI agent.
Key capabilities
- Chain-of-Thought Prompting
- Role-Playing Prompts
- Creative Text Format Generation
- Structured Output Formatting
Example prompts
- "Use chain-of-thought prompting to explain how photosynthesis works."
- "Assume the role of a seasoned detective and investigate this crime scene: [details]."
- "Write a haiku about autumn, using role-playing techniques to capture the feeling of melancholy."
Tips & gotchas
The effectiveness of these patterns depends on the underlying AI model's capabilities. Experiment with different prompts within each pattern to fine-tune results for your specific use case.
Tags
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