agentic-review

Evaluates LLM-generated outputs across architecture, naming, robustness, duplication, complexity, and conventions.

Updated Aug 27, 2026

Search Tags:#code quality #architecture #error handling #maintainability #naming conventions #agentic review

Ready to run

npx skills add https://github.com/CleanExpo/Pi-Dev-Ops --skill agentic-review-cleanexpo

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: agentic-review
Source: https://github.com/CleanExpo/Pi-Dev-Ops/tree/main/skills/agentic-review
Command: npx skills add https://github.com/CleanExpo/Pi-Dev-Ops --skill agentic-review-cleanexpo

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It helps you evaluate whether an agent’s output is not only correct, but also well-designed, maintainable, and reliably handled.

Core Features & Use Cases

Quality-focused review: Assesses architecture, naming, error handling, duplication, complexity, and conventions to catch weaknesses that correctness tests miss.
Structured improvement guidance: Translates review dimensions into actionable feedback that strengthens future iterations.
Use Case: After an autonomous coding run, you review the proposed changes to ensure the solution fits the project’s patterns and won’t degrade maintainability over time.

Quick Start

Ask an AI to run an “agentic-review” pass on the agent’s draft and score it across Architecture, Naming, Error handling, Duplication, Complexity, and Conventions, then provide concise improvement recommendations.

Frequently Asked Questions about agentic-review

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

FAQPage Schema

How do I review autonomous coding agent output for maintainability and design quality?▼

Reviewing autonomous coding agent output for maintainability requires scoring the generated code across architecture, naming clarity, error handling, DRY-ness, complexity, and project conventions. This structured quality-focused assessment catches design weaknesses that correctness tests miss.

What do code generation tests miss when evaluating AI agent refactoring results?▼

Code generation tests miss maintainability issues like poor architecture, weak error handling, and naming violations. A wider engineering review evaluates correctness alongside duplication, complexity minimization, and adherence to existing project conventions to prevent long-term degradation.

Can I evaluate AI-generated code for adherence to existing project conventions?▼

Yes, you can evaluate AI-generated code for adherence to existing project conventions by scoring the output against established patterns. This agentic review checks architecture, naming clarity, and DRY-ness to ensure the proposed changes fit the project.

How do I get actionable improvement feedback for autonomous coding drafts?▼

To get actionable improvement feedback for autonomous coding drafts, run an agentic review pass that translates design dimensions into concise recommendations. It scores architecture, error handling robustness, and complexity to strengthen future iterations.

When should I use an engineering review instead of just running tests on agent code?▼

You should use an engineering review instead of just running tests when you need to judge design quality and maintainability. Tests verify correctness, but reviewing architecture, duplication, and error handling ensures the solution won't degrade maintainability over time.