prompt-engineering-patterns

npx skills add sickn33/agentic-awesome-skills --skill prompt-engineering-patterns --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

# Prompt Engineering Patterns Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability. ## Do not use this skill when - The task is unrelated to prompt engineering patterns - You need a different domain or tool outside this scope ## Instructions - Clarify goals, constraints, and required inputs. - Apply relevant best practices and validate outcomes. - Provide actionable steps and verification. - If detailed examples are required, open `resources/implementation-playbook.md`. ## Use this skill when - 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 ## Core Capabilities ### 1. Few-Shot Learning - Example selection strategies (semantic similarity, diversity sampling) - Balancing example count with context window constraints - Constructing effect