production-resilience-reviewer

Review production code and system designs for failure modes and resilience fixes.

2|Updated Feb 26, 2026

Search Tags:#code review #system design #security analysis #production readiness #resilience assessment

Ready to run

npx skills add https://github.com/tomazb/agent-skills --skill production-resilience-reviewer

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: production-resilience-reviewer
Source: https://github.com/tomazb/agent-skills/tree/main/production-resilience-reviewer
Command: npx skills add https://github.com/tomazb/agent-skills --skill production-resilience-reviewer

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires python, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill assesses production code and system designs for potential failure modes and recommends actionable fixes, focusing on production resilience and minimizing the impact of failures.

Core Features & Use Cases

Twelve Failure Lenses: Thoroughly analyzes dependency, load, network, data, retry, debuggability, observability, change management, disaster recovery, security, quota, and complexity tax.
Severity Calibration: Prioritizes fixes based on impact, likelihood, blast radius, and detectability.
Two Review Modes: Quick Mode for code snippets and Full Mode for comprehensive system reviews.
AI-Generated Code Detection: Identifies common blind spots in AI-generated code.
Review Output: Provides evidence, recommendations, validation steps, monitoring plans, and priority rankings.
Use Case: A software engineer can use this Skill to review a critical service and identify potential vulnerabilities or bottlenecks before deployment.

Quick Start

Run the production-resilience-reviewer skill on the service 'checkout-service'.

Frequently Asked Questions about production-resilience-reviewer

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

FAQPage Schema

How do I review production code for resilience and potential failure modes?▼

To review production code for resilience, analyze it through twelve failure lenses covering dependencies, load handling, and data consistency. This process identifies potential failure modes and provides prioritized, actionable recommendations to minimize failure impact.

What is the best way to assess system design for production readiness?▼

The best way to assess system design for production readiness is to evaluate critical path analysis, error handling, and recovery strategies. A full review mode prioritizes fixes based on impact, blast radius, and detectability to ensure system stability.

Can I use Python to analyze a code snippet for error handling and recovery strategies?▼

Yes, you can use Python to analyze code snippets for error handling and recovery strategies. A quick review mode parses snippet code to identify vulnerabilities, offering evidence and validation steps without requiring a comprehensive system review.

Does this resilience assessment identify blind spots in AI-generated code?▼

Yes, this resilience assessment identifies common blind spots in AI-generated code. It analyzes the code for hidden failure modes across network, retry, and complexity tax lenses, ensuring AI-generated components meet production resilience standards.

How do I prioritize fixes for external dependency failures in a critical service?▼

You can prioritize fixes for external dependency failures using severity calibration. This mechanism ranks issues based on impact, likelihood, blast radius, and detectability, allowing you to address the most critical bottlenecks before deployment.

What are the limitations of a quick mode production resilience review?▼

The limitation of a quick mode production resilience review is that it only evaluates isolated code snippets rather than the full system architecture. It provides immediate feedback on specific vulnerabilities but lacks comprehensive disaster recovery and change management analysis.