agent-review

Review AI agent codebases for architecture, design patterns, and error handling.

8|1|Updated Jan 19, 2026

Search Tags:#code quality #architecture #best practices #agent #observability #review

Ready to run

npx skills add https://github.com/igbuend/grimbard --skill agent-review-igbuend

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: agent-review
Source: https://github.com/igbuend/grimbard/tree/main/skills/agent-review
Command: npx skills add https://github.com/igbuend/grimbard --skill agent-review-igbuend

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps ensure AI agents are built with robust architecture, clear design patterns, effective error handling, and good observability, preventing common pitfalls and improving reliability.

Core Features & Use Cases

Architecture Review: Assesses folder structure, separation of concerns, and adherence to best practices.
Design Pattern Validation: Checks for correct implementation of tool design, agent loops, memory management, and state handling.
Error Handling & Observability: Evaluates the robustness of error management and the quality of logging and tracing.
Use Case: Use this skill to audit a newly developed AI agent's codebase before deployment to identify potential issues in its design and implementation.

Quick Start

Use the agent-review skill to audit the agent code located in the '/path/to/agent/project' directory.

Frequently Asked Questions about agent-review

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

FAQPage Schema

How do I audit AI agent code for best practices and architecture?▼

You audit AI agent code by reviewing the codebase against established patterns for folder structure, design, error handling, and observability. This identifies potential architectural issues and improves reliability before deployment.

What does an AI agent architecture review check for?▼

An AI agent architecture review checks for separation of concerns, correct folder structure, and adherence to best practices. It validates the implementation of tool design, agent loops, memory management, and state handling.

How do I evaluate error handling and observability in my AI agent?▼

You evaluate error handling and observability by analyzing the agent codebase to assess the robustness of error management and the quality of logging and tracing mechanisms against established design patterns.

Can I use this to review a newly developed AI agent codebase before deployment?▼

Yes, you can audit a newly developed AI agent codebase before deployment to identify potential issues in its architecture, design patterns, and implementation to prevent common pitfalls.

What is the best way to validate agent loops and memory management in AI agents?▼

The best way to validate agent loops and memory management is to analyze the agent codebase and check the implementation against established design patterns for state handling and tool design.

Why does my AI agent lack observability and robust error handling?▼

Your AI agent may lack observability and robust error handling if the codebase does not follow established design patterns for logging, tracing, and error management, which can be identified through an architecture review.