How does the skill detect architectural flaws?

It uses targeted search patterns to identify anti-patterns such as regex-based LLM parsing, f-string prompts, and lack of human-in-the-loop approval gates.

What is the 12-Factor Agents methodology?

It is a set of best practices designed to make AI agents more reliable and maintainable, focusing on principles like structured outputs, prompt ownership, and stateless reducers.

Which frameworks does this skill support?

While framework-agnostic, it includes specialized patterns for Python-based AI frameworks like Pydantic AI, LangGraph, and the Vercel AI SDK.

What is 'Structured Output' in the context of agents?

It refers to the practice of forcing LLMs to return schema-validated JSON (using Pydantic or similar) rather than raw strings, ensuring deterministic tool execution.

Can I use this for security auditing?

Yes, it identifies dangerous patterns like direct execution of LLM-generated code (eval/exec) which are significant security risks in agentic systems.

12-Factor Agent Architecture Analyzer

Name: 12-Factor Agent Architecture Analyzer
Author: existential-birds

byexistential-birds

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Data Science & ML

Audits and evaluates AI agent codebases for compliance with the 12-Factor Agents methodology to ensure production-grade reliability.

About

This skill provides a comprehensive framework for reviewing LLM-powered systems and agentic applications against the industry-standard 12-Factor methodology. It automates the detection of common architectural anti-patterns—such as unvalidated LLM outputs, hardcoded prompts, and unbounded context windows—while checking for robust features like human-in-the-loop triggers and unified execution state. It is an essential tool for architects and developers building production AI systems who need to ensure their agentic architectures are scalable, maintainable, and deterministic.

Key Features

Automated pattern matching for common AI agent anti-patterns
Evaluation of prompt management and context window optimization
Analysis of structured output validation using Pydantic and dataclasses
Comprehensive 12-Factor compliance auditing for agentic systems
Assessment of launch/pause/resume capabilities and state unification
14 GitHub stars

Use Cases

Evaluating third-party agentic frameworks for best practice compliance
Auditing legacy AI systems for reliability and maintainability gaps
Performing pre-production architectural reviews for LLM-powered applications

About

Key Features

Automated pattern matching for common AI agent anti-patterns
Evaluation of prompt management and context window optimization
Analysis of structured output validation using Pydantic and dataclasses
Comprehensive 12-Factor compliance auditing for agentic systems
Assessment of launch/pause/resume capabilities and state unification
14 GitHub stars

Use Cases

Evaluating third-party agentic frameworks for best practice compliance
Auditing legacy AI systems for reliability and maintainability gaps
Performing pre-production architectural reviews for LLM-powered applications