Agent skill · writing content · aiskillstore
prompt-engineering-patterns
Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, or designing production prompt templates.
Why this skill is useful
Provides advanced techniques for optimizing prompt performance and consistency that the AI wouldn't reliably generate on its own.
What it needs
About 4k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
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 effective demonstrations with input-output pairs Dynamic example retrieval from knowledge bases Handling edge cases through strategic example selection 2. Chain-of-Thought Prompting Step-by-step reasoning elicitation Zero-shot CoT with "Let's think step by step" Few-shot CoT with reasoning traces Self-consistency techniques (sampling multiple reasoning paths) Verification and validation steps 3. Prompt Optimization Iterative refinement workflows A/B testing prompt variations Measuring prompt performance metrics (accuracy, consistency, latency) Reducing token usage while maintaining quality Handling edge cases and failure modes 4. Template Systems Variable interpolation and formatting Conditional prompt sections Multi-turn conversation templates Role-based prompt composition Modular prompt components 5. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/prompt-engineering-patterns