What is the 10-Layer Architecture used by this skill?

The 10-Layer Architecture is a structured framework for professional prompt engineering that evaluates key components such as persona, task context, constraints, examples, and output formatting.

Is this tool specific to Claude prompts only?

While optimized for use within the Claude Code environment, the structural analysis provided by the 10-Layer Architecture applies to prompts intended for any major Large Language Model.

Does this skill modify my source files?

No, the review-prompt skill is designed to provide detailed analysis and feedback without making any changes to your existing files.

How do I trigger a prompt review in Claude Code?

You can invoke the skill by using the command 'review-prompt' followed by the prompt content or file reference you want the agent to analyze.

Does it provide suggestions for improvement?

Yes, the skill provides a detailed assessment that highlights specific areas where the prompt can be strengthened for better AI performance.

Prompt Architecture Reviewer

Name: Prompt Architecture Reviewer
Author: doodledood

bydoodledood

•

Data Science & ML

Analyzes and evaluates LLM prompts using a structured 10-Layer Architecture to improve clarity, precision, and output performance.

About

This skill provides a non-destructive assessment of Large Language Model prompts, leveraging the comprehensive 10-Layer Architecture framework. It allows developers to identify weaknesses in their prompt logic, structure, and constraints without altering the original source files. It is an essential tool for prompt engineers and developers looking to optimize AI agent performance, reduce hallucinations, and ensure high-quality, predictable outputs from their LLM integrations within the Claude Code environment.

Key Features

Non-destructive analysis that preserves original prompt and code files
Seamless integration with agentic development workflows in Claude Code
Actionable feedback for improving prompt precision and reliability
Comprehensive assessment based on the 10-Layer Prompt Architecture framework
Identification of structural weaknesses, missing context, and vague instructions
8 GitHub stars

Use Cases

Refining system prompts to reduce model hallucination and improve instruction following
Auditing complex agentic prompts before production deployment to ensure robustness
Standardizing prompt engineering patterns across a development team

About

Key Features

Non-destructive analysis that preserves original prompt and code files
Seamless integration with agentic development workflows in Claude Code
Actionable feedback for improving prompt precision and reliability
Comprehensive assessment based on the 10-Layer Prompt Architecture framework
Identification of structural weaknesses, missing context, and vague instructions
8 GitHub stars

Use Cases

Refining system prompts to reduce model hallucination and improve instruction following
Auditing complex agentic prompts before production deployment to ensure robustness
Standardizing prompt engineering patterns across a development team