Llm Patterns

What it does

This skill provides a collection of reusable patterns and templates specifically designed to optimize interactions with Large Language Models (LLMs). It helps structure complex requests, define clear constraints, and standardize output formats for more reliable agent behavior.

When to use it

• You need to enforce strict formatting rules, such as JSON or Markdown tables, in your agent's responses.
• You are building multi-step workflows where the LLM must maintain context across several turns without losing focus.
• You want to test different prompting strategies (e.g., few-shot learning, chain-of-thought) to see which yields better results for a specific task.

Key capabilities

• Structured Output Templates: Pre-built schemas to force consistent data formats from the model.
• Context Management Patterns: Techniques to preserve long-term memory and state within a session.
• Role-Playing Frameworks: Defined personas to guide the tone and expertise level of the AI agent.
• Constraint Enforcement: Specific phrasing patterns to prevent hallucinations or deviation from safety guidelines.

Example prompts

• "Apply the 'JSON Mode' pattern to extract only the customer's name and order ID from this unstructured text."
• "Use the 'Chain-of-Thought' pattern to analyze these sales figures before generating a recommendation."
• "Activate the 'Expert Critic' persona to review my draft code for security vulnerabilities."

Tips & gotchas

Ensure you have sufficient context window remaining when applying complex multi-step patterns, as they consume more tokens. Test each pattern with varied inputs to verify it handles edge cases before deploying it in production workflows.