cosmic-python

Cosmic Python: Code Architecture for Production Systems

⚠️ CRITICAL DISTINCTION

Cosmic Python is NOT about system architecture (service boundaries, deployment topology, C4 models—that's the separate architecture skill).

Cosmic Python IS about code structure within a Python module or service: how to organize classes, functions, tests, and dependencies so that code is clean, testable, and maintainable.

When to use Cosmic Python:

• You have a service/module to build (the strategic scope is clear from architecture decisions)
• You need to structure the Python code inside that service with clean layers
• You want to enforce clean code principles, SOLID, and dependency management
• You need comprehensive test coverage organized per layer

This skill pairs with:

• Stream Coding (documentation-first methodology) – Use to plan WHAT to build
• Cosmic Python (this skill) – Use to structure HOW to build the code
• Architecture skill – Use for system-level design (separate scope)

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THE MEANINGFY CONTRACT: MINIMISE WTFs PER MINUTE

Our goal is clean code that passes code review quickly while keeping developers productive and safe.

This is achieved through three non-negotiable commitments:

1. Layered Architecture – Strict Separation of Concerns

Within each Python module or service, separate code into four tightly-bounded layers:

• models/ – Domain logic (business rules, entities, value objects)

  • No I/O, no framework dependencies, pure domain
  • Tests focus on business invariants and domain rules
  • Fastest tests, run first

• adapters/ – Infrastructure and integration (databases, APIs, file systems)

  • Implement repositories, gateways, clients
  • Depend on models only; never on services or entrypoints
  • Tests mock external services; focus on integration boundaries

• services/ – Use-case orchestration (application logic, workflows)

  • Choreograph models and adapters
  • Contain transaction boundaries and error handling policy
  • May depend on models and adapters, never on entrypoints
  • Tests use mocks for adapters; focus on orchestration logic

• entrypoints/ – Request/response boundaries (API, CLI, schedulers, workers)

  • Parse input, call services, format responses
  • Handle routing, status codes, argument validation
  • Minimal business logic; delegate to services
  • Tests verify contracts (status codes, response shapes, argument parsing)

2. Dependency Direction – Always Enforced (DIP)

The Law:

entrypoints → services → models
              ↘
              adapters → models

Never the reverse. High-level policy (models + services) must never depend on low-level details (adapters). Low-level details depend on abstractions (interfaces/protocols), not the other way around.

When you violate this:

• Circular dependencies appear
• Models become framework-dependent
• Testing becomes hard (can't mock cleanly)
• You've broken the architecture

How to enforce:

• Use importlinter in CI/CD to block forbidden imports (see references)
• Code review checklist: "What imports what?" before approving

3. No Clean Code Without Tests – Every Layer Owns Its Tests

"If you can't test it, you can't understand it. If you can't understand it, you can't maintain it."

• 80%+ coverage on production code (target per layer)
• Tests per layer: Each layer tests its own responsibility, not other layers' internals
• TDD/BDD: Write tests before code; BDD (Gherkin) for use cases
• Test pyramid: Many unit tests (fast, isolated), fewer integration tests, minimal end-to-end

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CORE PRINCIPLES FROM MEANINGFY ENGINEERING

Clean Code Standards

• Intention-revealing names – calculate_customer_tier() not calc_tier()
• Small, cohesive functions – <25 lines is a good target; if you say "and" in description, split
• No magic strings – Define constants or enums; raw string literals are technical debt
• Minimal nesting – Deep nesting signals complexity; refactor
• DRY but not obsessively – Three similar lines don't warrant a helper; ten do

SOLID Principles Applied Systematically

• SRP – One responsibility per class/function; if multiple reasons to change, split
• OCP – Extend via new classes/strategies, not conditional logic in existing code
• LSP – Subclasses must respect contracts; avoid overrides that break expected behavior
• ISP – Avoid "fat" interfaces; split into cohesive ones matching clients' needs
• DIP – Depend on abstractions (interfaces/protocols), inject concrete implementations

Observability as First-Class

• Structured logging – Logs are data, not strings; emit JSON with context
• Keep it in the right layers – Observability belongs in services and entrypoints, not deep in models
• OpenTelemetry patterns – Tracing and metrics via standard conventions
• No print statements in production – Use logging framework always

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ONE-MINUTE CODE STRUCTURE CHECK

For each function/class, ask:

• Which layer does this belong in?
• What should it depend on?
• What should depend on it?
• What tests does it need?
• Does it have only one reason to change?

If you can't answer these clearly, the architecture is drifting. See the references for detailed refactoring paths.

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COMMON WORKFLOWS

Workflow 1: Implementing Code From Specs (Stream Coding Phase 3)

When you have clear specs from Stream Coding Phase 2 (doc is 9+/10 Clarity Gate):

1. Identify which layer each component belongs to – Models? Services? Adapters?
2. Start with models/ – Pure domain logic, no I/O, no frameworks
3. Write unit tests for models first – Verify domain rules before implementing services
4. Implement adapters/ – Repositories, gateways, clients; mock external services in tests
5. Implement services/ – Orchestrate models and adapters; test with mocked adapters
6. Implement entrypoints/ – CLI, API, schedulers; minimal logic, mostly delegation
7. Run full test suite – Verify 80%+ coverage per layer
8. Run architectural checks – make check-architecture to validate import contracts

Workflow 2: Code Review Against Cosmic Python Standards

Before approving a pull request, ask:

Layering:

• Does models/ import from services, adapters, or entrypoints? ❌ Should not
• Do adapters/ import from services or entrypoints? ❌ Should not
• Do services/ import from entrypoints? ❌ Should not
• Is business logic in services/ or models/, not scattered in entrypoints/? ✅ Should be

Clean Code:

• Are functions <25 lines? ✅ Target for readability
• Do all functions have only one reason to change? ✅ Check for SRP violation
• Are all symbolic identifiers (role names, status values, etc.) constants/enums, not magic strings? ✅ Required
• Is there any deep nesting (3+ levels)? ❌ Refactor to smaller functions

Testing:

• Does each layer have appropriate unit tests? ✅ Required
• Are important decisions (branches, error cases) tested? ✅ All branches covered
• Do adapters use mocks for external systems? ✅ Tests must be isolated
• Is coverage reported per layer? ✅ Verify with coverage report

Observability:

• Are logs in services/ and entrypoints/, not deep in models/? ✅ Correct placement
• Is logging structured (JSON context), not formatted strings? ✅ Required for production

Workflow 3: Test Organization Per Layer

For each layer, write tests that match its responsibility:

Layer	What to Test	How	Example
models/	Domain rules, invariants, transformations	Unit tests, no I/O, fast	test_user_cannot_have_negative_balance()
adapters/	Integration with external systems (mocked)	Unit tests with mocks, or integration with real test DBs	test_postgres_repository_insert_user()
services/	Use-case orchestration and business workflows	Unit tests with mocked adapters	test_user_signup_flow_sends_verification_email()
entrypoints/	Request parsing, response formatting, status codes	Unit tests, check contracts	test_api_endpoint_returns_201_on_create()

Target: 80%+ coverage overall, with focus on each layer's responsibility (not mixing concerns).

Workflow 4: Spotting and Fixing Architecture Drift

Common anti-patterns and how to fix them:

Anti-Pattern	How It Looks	Why It's Wrong	Fix
I/O in models	import requests in models/user.py	Models can't be tested in isolation	Move HTTP call to adapters/, inject via DIP
Business rules in entrypoints	API handler validates and transforms data	Logic is scattered, untestable	Extract to services/, call from handler
Circular imports	services/ → adapters/ → services/	Can't import cleanly, hard to test	Restructure: adapters/ → models/; services/ → adapters/ + models/
Magic strings everywhere	if user.role == "admin" in 5 files	Refactoring is fragile; intent hidden	Define ROLE_ADMIN = "admin" constant once, import everywhere
No tests for branching	services/ has 5 branches but only happy path tested	Edge cases crash production	Add parametrized tests for each branch
Clever one-liners	[x for x in y if x.z and (a or b)]	Unreadable; maintenance nightmare	Expand to 3-4 readable lines with intermediate variables

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QUALITY ASSURANCE & TOOLING

Essential Tools for Well-Guarded Projects

• Poetry – Dependency management, lockfiles, reproducible builds
• pytest + pytest-bdd – Unit tests (TDD) and behavior specs (BDD/Gherkin)
• tox – Test multiple Python versions in isolated environments
• importlinter – Enforce architectural boundaries (block forbidden imports)
• pylint / flake8 – Static analysis, style compliance
• SonarQube / SonarCloud – Code quality gates, duplication, security smells
• Codecov – Coverage reporting, trend tracking, gate on new code

Typical Makefile Pattern

make install                # Set up environment (poetry install)
make test                   # Run unit tests with coverage
make test-bdd               # Run BDD feature tests
make check-architecture     # Validate import contracts (importlinter)
make lint                   # Run style checks (pylint, flake8)
make ci                     # Full pipeline (all above)

CI/CD Workflow

Before merging to main:

1. Run tests and verify 80%+ coverage on new code
2. Run importlinter to block dependency violations
3. Run SonarCloud analysis; no new critical issues
4. Verify Clarity Gate from spec still matches code

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PRINCIPLES & BEST PRACTICES

On Code Structure

• Organize by domain, not by layer – billing/models/, billing/services/, not models/billing/
• Use constants/enums for all symbolic identifiers – Never rely on "magic strings"
• Small, cohesive functions – If you say "and" when describing what a function does, split it
• Avoid deep nesting – >3 levels usually signals refactoring opportunity
• DRY but pragmatically – 2-3 similar lines are OK; 5+ warrant extraction

On Testing

• Test the decisions, not the steps – Test what varies (branches), not happy paths
• Test per layer, not per unit – Models test domain rules; services test orchestration
• Use fixtures and parametrization – pytest.mark.parametrize for multiple scenarios
• Mock external dependencies – Adapters provide mocks for services tests
• BDD for use cases – Gherkin feature files for end-to-end workflows

On Architecture Decisions

• Document major choices in ADRs – Architecture Decision Records explain the trade-off
• 5–8 ADRs per service is typical – More suggests decisions are tangled
• Contract-first design – Write OpenAPI/AsyncAPI specs before code
• Use dependency injection – Never import concrete implementations directly in services

On Observability

• Structured logging – Emit JSON with context, not formatted strings
• Keep observability in the right layers – services/ and entrypoints/, not deep in models/
• No print() in production – Use logging framework; configure per environment
• OpenTelemetry for tracing – Standard conventions for metrics and spans

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WHEN NOT TO USE COSMIC PYTHON

• Rapid prototypes – Layering has upfront cost; worthwhile only if long-term maintenance matters
• Exploratory/spike code – Keep spikes separate; migrate to Cosmic Python only when strategy is clear
• Simple scripts – Single-file scripts don't need four layers

This approach requires team discipline. One developer ignoring layers breaks the architecture for everyone. All developers must respect boundaries and code review rigorously.

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COSMIC PYTHON + STREAM CODING: THE COMPLETE WORKFLOW

Stream Coding (documentation-first planning) and Cosmic Python (clean code structure) work together:

Phase	Methodology	Focus	Output
1	Stream Coding	Strategic: WHAT to build, WHY	Strategic Blueprint + ADRs
2	Stream Coding	Specifications: HOW to build (AI-ready)	Implementation Specs (9+/10 Clarity Gate)
3	Cosmic Python	Code: Implement following layers/SOLID	Production code (80%+ tested)
4	Cosmic Python	Quality: Prevent drift, maintain specs	CI/CD gates, spec-first fixes

The Integration

• Phase 2 specs must reference Cosmic Python layers – "Models layer will contain...", "Services layer will orchestrate..."
• Phase 3 code follows Cosmic Python patterns – Layering, SOLID, testing per layer
• Phase 4 maintenance – When fixing bugs, update spec first, then regenerate code (not manual patches)

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REFERENCE MATERIALS

Layer-by-Layer Real-World Examples

• example-models-layer.md – Domain entities, value objects, Pydantic models, no I/O
• example-adapters-layer.md – Repositories, gateways, abstract interfaces, test doubles
• example-services-layer.md – Use-case orchestration, dependency injection, error handling patterns
• example-entrypoints-layer.md – FastAPI endpoints, Typer CLI, scheduler integration

Testing & Quality

• example-testing-patterns.md – Unit tests per layer, mocking strategy, fixtures, BDD patterns
• example-project-quality-ci.md – Makefile, tox, importlinter, GitHub Actions, SonarCloud setup

Advanced Production Patterns

• example-advanced-patterns.md – Configuration via decorators, exception hierarchies, protocols, cross-version services

Documentation-First Methodology (Stream Coding)

• stream-coding-methodology.md – 40/40/20 time split, 4 mandatory spec sections, Clarity Gate, Rule of Divergence
• phase-1-strategic-blueprint-checklist.md – 7 Questions framework, Strategic Blueprint, ADR writing
• phase-2-clarity-gate-checklist.md – AI-ready specs, 13-item Clarity Gate, scoring rubric

Company Standards & Principles

• MEANINGFY_PROMPT.md – Full Meaningfy engineering culture: project structure, layering rules, SOLID principles, CI/CD expectations, security

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SKILL RELATIONSHIPS

• architecture ← System-level design (C4 models, service boundaries, deployment); use BEFORE Cosmic Python
• stream-coding ← Documentation-first methodology (planning specs); use WITH Cosmic Python for Phase 3 implementation
• cosmic-python ← Code structure within services (this skill)
• Your CI/CD tooling → importlinter, SonarCloud, pytest, tox (configured per Cosmic Python standards)