architecting-python

Enforce architecture decision records with testing strategy for Python projects.

1|Updated Jan 6, 2026

Search Tags:#testing #architecture #python #adr #ddd #dependency-injection #hexagonal-architecture

Ready to run

npx skills add https://github.com/outcomeeng/claude --skill architecting-python-outcomeeng

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: architecting-python
Source: https://github.com/outcomeeng/claude/tree/main/plugins/python/skills/architecting-python
Command: npx skills add https://github.com/outcomeeng/claude --skill architecting-python-outcomeeng

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Architecture decisions for Python projects are often ad-hoc and inconsistently documented. This skill provides a formal ADR-driven framework that enforces testing alignment, clear boundaries, and a review workflow to ensure durable architectural outcomes.

Core Features & Use Cases

Defines a standardized ADR workflow for Python projects, including context, decision, and consequences sections.
Enforces testing-first governance by mandating a Testing Strategy within each ADR and alignment with /testing principles.
Promotes clean architecture principles (DI, hexagonal/DDD patterns) and explicit boundary definitions to support maintainability and scalability.

Quick Start

Draft ADRs using the template and submit them to the reviewer for validation.

Frequently Asked Questions about architecting-python

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

FAQPage Schema

How do I document Python architecture decisions to enforce testing alignment?▼

Architecture decision records (ADRs) for Python projects enforce testing alignment by mandating a Testing Strategy section within each ADR, ensuring traceable compliance with project testing principles and clear boundary definitions.

What is the best way to structure ADRs for Python projects using hexagonal architecture?▼

ADRs for Python projects structure hexagonal architecture decisions by defining context, decision, and consequences sections, explicitly outlining dependency injection patterns and domain-driven design boundaries to support maintainability and scalability.

Can I use dependency injection patterns in Python without a formal review workflow?▼

Dependency injection patterns can be used independently, but integrating them through an ADR reviewer workflow validates that DI constraints and testing strategies are traceably documented, ensuring durable architectural outcomes for Python systems.

How do I apply domain-driven design boundaries in Python with strict testing requirements?▼

Applying domain-driven design boundaries in Python requires defining explicit architecture constraints in an ADR, specifying the testing strategy for those boundaries, and submitting the record to a reviewer workflow for validation.

Why does my Python architecture lack traceable compliance across testing and design decisions?▼

Python architecture lacks traceable compliance when architecture decisions are ad-hoc and undocumented, preventing alignment between testing strategies and clean architecture principles like dependency injection and hexagonal patterns.