Can this skill help prevent AI hallucinations?

Yes, it includes specific quality check patterns and uncertainty expressions (like 'I don't know') to reduce guessing and improve factual accuracy.

Does it support structured output formats?

Absolutely. It provides patterns for enforcing specific structures such as JSON, Markdown sections, and custom templates for easy integration into other software.

When should I use this skill?

Use it when Claude's outputs are inconsistent, when you need strict formatting like JSON, or when a task requires multi-step reasoning that needs explicit structure.

What is Meta Prompt Engineering?

It is a systematic methodology for designing prompts that use roles, task decomposition, and explicit constraints to ensure an AI model produces the highest quality results consistently.

Meta Prompt Engineering

Name: Meta Prompt Engineering
Author: lyndonkl

bylyndonkl

•

Data Science & ML

Transforms vague instructions into structured, constraint-aware prompts for consistent and high-quality AI outputs.

About

The Meta Prompt Engineering skill provides a systematic framework for optimizing how Claude processes tasks by applying professional prompt engineering patterns. It helps developers move beyond basic instructions by implementing expert personas, task decomposition, strict formatting constraints, and self-evaluation guardrails. This skill is essential for anyone building production-grade AI workflows that require high reliability, predictable formatting, and domain-specific expertise encoding.

Key Features

Task decomposition for multi-step reasoning and complex logic
Structured output definitions for JSON, Markdown, and templates
Built-in quality checks and self-correction rubrics
Persona and role framing for domain-specific expertise
8 GitHub stars
Strict constraint enforcement for tone, length, and safety

Use Cases

Encoding domain expertise and safety guardrails into complex workflows
Standardizing inconsistent model outputs into structured data formats
Developing reusable prompt templates for production AI applications

About

Key Features

Task decomposition for multi-step reasoning and complex logic
Structured output definitions for JSON, Markdown, and templates
Built-in quality checks and self-correction rubrics
Persona and role framing for domain-specific expertise
8 GitHub stars
Strict constraint enforcement for tone, length, and safety

Use Cases

Encoding domain expertise and safety guardrails into complex workflows
Standardizing inconsistent model outputs into structured data formats
Developing reusable prompt templates for production AI applications