# Case Study: Spec-to-Code Semantic Review Skill

## Overview
This case study describes an AI skill that formalizes senior software engineers'
design review expertise into a human-in-the-loop workflow using Large Language Models (LLMs).

Rather than automating decisions, the skill augments human judgment by surfacing
semantic inconsistencies between software specifications and source code.

## Problem
Design-code consistency reviews rely heavily on senior engineers' tacit knowledge.
This creates bottlenecks, review quality variance, and scaling limitations.

## Skill-Based Approach
The solution reframes design review as a transferable AI skill consisting of:
- Structured specification inputs
- Annotated source code inputs
- Explicit review criteria
- LLM-based reasoning
- Human final judgment

## Human-in-the-Loop Design
LLMs generate review findings and explanations.
Humans validate, contextualize, and decide outcomes.

This ensures safety, accountability, and business alignment.

## Outcome
- Reduced review time
- Improved consistency
- Preservation of human responsibility

This aligns with Anthropic's principles of safe, controllable AI deployment.
