expert-entropy-auditor

Identify gaps between design intent and code implementation across repositories.

13|3|Updated Apr 7, 2026
One-click install
npx skills add https://github.com/wonderslife/pdd-skills-v3 --skill expert-entropy-auditor
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: expert-entropy-auditor
Source: https://github.com/wonderslife/pdd-skills-v3/tree/main/skills/entropy/expert-entropy-auditor
Command: npx skills add https://github.com/wonderslife/pdd-skills-v3 --skill expert-entropy-auditor

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Detect gaps between design intent and code implementation to surface architectural drift and AI residues that erode software quality.

Core Features & Use Cases

  • Surface PRD and Spec inconsistencies by cross-checking requirements with code implementations.
  • Identify scattered AI residue and recommend consolidating into shared utilities to reduce entropy.
  • Generate actionable audit reports and prioritized improvement plans for engineering teams.

Quick Start

Audit the repository to surface gaps between design intent and implementation and consolidate AI residue into reusable utilities.

Frequently Asked Questions about expert-entropy-auditor

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I detect AI residue in my codebase?

AI residue in a codebase is detected by cross-checking design intent with code implementation to surface architectural drift. This identifies scattered AI-generated code and recommends consolidating it into shared utilities to reduce entropy.

What is PRD consistency analysis in software engineering?

PRD consistency analysis is the process of cross-checking PRD requirements against actual code implementations. It surfaces gaps between design intent and source code to produce structured audit reports and actionable improvement suggestions.

Can I perform static analysis for technical debt across multi-language repositories?

Static analysis for technical debt can be performed across multi-language repositories by auditing source code against specs. This handles projects of varying sizes and outputs prioritized improvement plans for engineering teams.

How do I audit spec consistency against source code?

Spec consistency is audited by cross-checking specifications and PRDs with code implementations to identify architectural drift. This generates structured audit reports defining audit dimensions and providing actionable suggestions.

What is the best way to reduce code entropy from AI-generated snippets?

Code entropy from AI-generated snippets is reduced by identifying scattered AI residue and recommending consolidation into shared utilities. This unifies redundant implementations and generates a prioritized improvement plan.