quick-brown-foxxx/architecting-python-changes

Architecting Python Changes

This skill is the first stop for non-trivial Python architecture decisions. It is both a compact guide and a router to deeper project docs and domain skills.

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Default Approach

1. Classify the change:
   • small local fix
   • feature adding new behavior or a new caller
   • refactor moving responsibilities or dependencies
   • project setup or framework choice
2. Ask yourself what is actually expected to change later:
   • business rules
   • validation or transport
   • UI or API surface
   • infrastructure or third-party dependency
   • platform support
   • workflow observability or operational controls
3. Identify the real architecture question:
   • where should this code live?
   • what deserves a boundary or wrapper?
   • should this be reusable from CLI, GUI, API, or automation?
   • is a boring framework or library better than custom code here?
   • is inheritance being used where composition would be clearer?
4. Apply the heuristics below.
5. Load the matching deeper source, then continue with implementation skills.

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Core Heuristics

Boundaries and layers

• Keep a small fix local unless the bug was caused by the wrong boundary.
• Extract shared behavior downward into domain/utilities instead of duplicating across entry points or logic/flow branches.
• Keep business rules separate from validation plumbing, transport shapes, auth/session concerns, and raw infrastructure.
• Wrap third-party or OS-specific boundaries when typing, exception isolation, portability, or replacement matters.

Reuse versus custom code

• Prefer boring popular maintained libraries and frameworks for commodity infrastructure: auth, packaging, builds, queues, caching, deployment, migrations, or orchestration.
• Do not build ad hoc systems on top of a tiny framework when the domain already implies substantial backend concerns.
• For tiny one-off helpers or stable details, simple custom code is often better than heavyweight scaffolding.
• When selecting a library, do web research and compare multiple options, consider how each one fits philosophy and architecture, remember to analyze for typing support.

Composition and state

• Prefer composition, explicit data flow, and small protocol-shaped interfaces over deep class hierarchies.
• Add stateful classes for lifecycle or orchestration, not to manufacture abstractions.
• Hidden mutable state is a smell. Important state changes should be visible in code.

Reusable cores

• If CLI, GUI, API, or automation may share logic, build a reusable core plus thin adapters.
• Start with one useful implementation, but do not trap business logic inside the first interface.
• Prefer composable pieces over one giant super-tool.

Transparency and operations

• Important workflows should be inspectable: clear validation, explicit errors, logs at operation boundaries, and dry-run behavior where practical.
• For risky multi-step actions, prefer explicit step/state models and machines over opaque helper chains.
• If a failure would be costly or hard to debug for user, design for traceability early instead of bolting it on later.

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Where To Look

• docs/PHILOSOPHY.md

  • First source for the project's core architecture rules: dependency direction, reusable cores, composition, wrappers at dynamic boundaries, and transparency.

• docs/coding_rules.md

  • Code-facing rules that back the philosophy: wrapper enforcement, circular-import handling, architecture boundaries, error boundaries, and layout expectations.

• setting-up-python-projects

  • Use when the question is high-level repo/package structure, project shape, scaffolding level, framework choice, or generic architecture choices.

• setting-up-python-backends

  • Use when the repo is primarily a backend, API service, or worker-oriented system and you need backend-specific scaffolding, migrations, health endpoints, app factory choices, or service-first layout defaults.

• building-python-backends

  • Use when the architecture question is about backend/service layering, transport vs domain boundaries, transactions, auth/session edges, workers, or important operation flows.

• building-multi-ui-apps

  • Use when CLI, GUI, API, or automation share business logic and you need adapters, a composition root, or interface separation.

• building-qt-apps

  • Use when the architecture question is specific to PySide6 or Qt: manager vs service vs wrapper, event-loop boundaries, signals, or platform integration.

• writing-python-code

  • Use after the structure is chosen, or when the decision is really about implementing typed wrappers, handling import cycles correctly, or encoding the boundary in code.

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Common Mistakes

• Extracting layers because the pattern exists, not because responsibilities or change axes differ.
• Mixing domain rules with request/response models, validation glue, or infrastructure calls.
• Letting the first UI own logic that later needs to be shared.
• Introducing inheritance where a couple of collaborating objects would be clearer.
• Building custom infrastructure in a domain that already has strong boring solutions.

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Handoff

• After the architecture direction is clear, continue with writing-python-code.
• If the work is a brand-new project or major re-shape, also consult setting-up-python-projects.
• If the change needs a formal plan, write it in the project's normal planning flow before implementation.