Python Patterns

Overview

This public intake copy packages

plugins/antigravity-awesome-skills-claude/skills/python-patterns

https://github.com/sickn33/antigravity-awesome-skills

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses

metadata.json

ORIGIN.md

Python Patterns > Python development principles and decision-making for 2025. > Learn to THINK, not memorize patterns.

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: ⚠️ How to Use This Skill, 1. Framework Selection (2025), 2. Async vs Sync Decision, 3. Type Hints Strategy, 7. Background Tasks, 10. Decision Checklist.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• Use this skill when making Python architecture decisions, choosing frameworks, designing async patterns, or structuring Python projects.
• Use when the request clearly matches the imported source intent: Python development principles and decision-making. Framework selection, async patterns, type hints, project structure. Teaches thinking, not copying.
• Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.
• Use when provenance needs to stay visible in the answer, PR, or review packet.
• Use when copied upstream references, examples, or scripts materially improve the answer.
• Use when the workflow should remain reviewable in the public intake repo before the private enhancer takes over.

Operating Table

metadata.json

ORIGIN.md

SKILL.md

SKILL.md

## Related Skills

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
2. Read the overview and provenance files before loading any copied upstream support files.
3. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
4. Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
5. Validate the result against the upstream expectations and the evidence you can point to in the copied files.
6. Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
7. Before merge or closure, record what was used, what changed, and what the reviewer still needs to verify.

Imported Workflow Notes

Imported: ⚠️ How to Use This Skill

This skill teaches decision-making principles, not fixed code to copy.

• ASK user for framework preference when unclear
• Choose async vs sync based on CONTEXT
• Don't default to same framework every time

Examples

Example 1: Ask for the upstream workflow directly

Use @python-patterns to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @python-patterns against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @python-patterns for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @python-patterns using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Type - Purpose - Tools
• Unit - Business logic - pytest
• Integration - API endpoints - pytest + httpx/TestClient
• E2E - Full workflows - pytest + DB
• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
• Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
• Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.

Imported Operating Notes

Imported: 4. Project Structure Principles

Structure Selection

Small project / Script:
├── main.py
├── utils.py
└── requirements.txt

Medium API:
├── app/
│   ├── __init__.py
│   ├── main.py
│   ├── models/
│   ├── routes/
│   ├── services/
│   └── schemas/
├── tests/
└── pyproject.toml

Large application:
├── src/
│   └── myapp/
│       ├── core/
│       ├── api/
│       ├── services/
│       ├── models/
│       └── ...
├── tests/
└── pyproject.toml

FastAPI Structure Principles

Organize by feature or layer:

By layer:
├── routes/ (API endpoints)
├── services/ (business logic)
├── models/ (database models)
├── schemas/ (Pydantic models)
└── dependencies/ (shared deps)

By feature:
├── users/
│   ├── routes.py
│   ├── service.py
│   └── schemas.py
└── products/
    └── ...

Imported: 5. Django Principles (2025)

Django Async (Django 5.0+)

Django supports async:
├── Async views
├── Async middleware
├── Async ORM (limited)
└── ASGI deployment

When to use async in Django:
├── External API calls
├── WebSocket (Channels)
├── High-concurrency views
└── Background task triggering

Django Best Practices

Model design:
├── Fat models, thin views
├── Use managers for common queries
├── Abstract base classes for shared fields

Views:
├── Class-based for complex CRUD
├── Function-based for simple endpoints
├── Use viewsets with DRF

Queries:
├── select_related() for FKs
├── prefetch_related() for M2M
├── Avoid N+1 queries
└── Use .only() for specific fields

Imported: 6. FastAPI Principles

async def vs def in FastAPI

Use async def when:
├── Using async database drivers
├── Making async HTTP calls
├── I/O-bound operations
└── Want to handle concurrency

Use def when:
├── Blocking operations
├── Sync database drivers
├── CPU-bound work
└── FastAPI runs in threadpool automatically

Dependency Injection

Use dependencies for:
├── Database sessions
├── Current user / Auth
├── Configuration
├── Shared resources

Benefits:
├── Testability (mock dependencies)
├── Clean separation
├── Automatic cleanup (yield)

Pydantic v2 Integration

# FastAPI + Pydantic are tightly integrated:

# Request validation
@app.post("/users")
async def create(user: UserCreate) -> UserResponse:
    # user is already validated
    ...

# Response serialization
# Return type becomes response schema

Imported: 8. Error Handling Principles

Exception Strategy

In FastAPI:
├── Create custom exception classes
├── Register exception handlers
├── Return consistent error format
└── Log without exposing internals

Pattern:
├── Raise domain exceptions in services
├── Catch and transform in handlers
└── Client gets clean error response

Error Response Philosophy

Include:
├── Error code (programmatic)
├── Message (human readable)
├── Details (field-level when applicable)
└── NOT stack traces (security)

Imported: 9. Testing Principles

Testing Strategy

Async Testing

# Use pytest-asyncio for async tests

import pytest
from httpx import AsyncClient

@pytest.mark.asyncio
async def test_endpoint():
    async with AsyncClient(app=app, base_url="http://test") as client:
        response = await client.get("/users")
        assert response.status_code == 200

Fixtures Strategy

Common fixtures:
├── db_session → Database connection
├── client → Test client
├── authenticated_user → User with token
└── sample_data → Test data setup

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in

plugins/antigravity-awesome-skills-claude/skills/python-patterns

metadata.json

ORIGIN.md

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated

SKILL.md

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @prompt-engineer

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @prompt-engineering

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @prompt-engineering-patterns

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @prompt-library

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

references

references/n/a

examples

examples/n/a

scripts

scripts/n/a

agents

agents/n/a

assets

assets/n/a

Imported Reference Notes

Imported: 1. Framework Selection (2025)

Decision Tree

What are you building?
│
├── API-first / Microservices
│   └── FastAPI (async, modern, fast)
│
├── Full-stack web / CMS / Admin
│   └── Django (batteries-included)
│
├── Simple / Script / Learning
│   └── Flask (minimal, flexible)
│
├── AI/ML API serving
│   └── FastAPI (Pydantic, async, uvicorn)
│
└── Background workers
    └── Celery + any framework

Comparison Principles

Selection Questions to Ask:

1. Is this API-only or full-stack?
2. Need admin interface?
3. Team familiar with async?
4. Existing infrastructure?

Imported: 2. Async vs Sync Decision

When to Use Async

async def is better when:
├── I/O-bound operations (database, HTTP, file)
├── Many concurrent connections
├── Real-time features
├── Microservices communication
└── FastAPI/Starlette/Django ASGI

def (sync) is better when:
├── CPU-bound operations
├── Simple scripts
├── Legacy codebase
├── Team unfamiliar with async
└── Blocking libraries (no async version)

The Golden Rule

I/O-bound → async (waiting for external)
CPU-bound → sync + multiprocessing (computing)

Don't:
├── Mix sync and async carelessly
├── Use sync libraries in async code
└── Force async for CPU work

Async Library Selection

Imported: 3. Type Hints Strategy

When to Type

Always type:
├── Function parameters
├── Return types
├── Class attributes
├── Public APIs

Can skip:
├── Local variables (let inference work)
├── One-off scripts
├── Tests (usually)

Common Type Patterns

# These are patterns, understand them:

# Optional → might be None
from typing import Optional
def find_user(id: int) -> Optional[User]: ...

# Union → one of multiple types
def process(data: str | dict) -> None: ...

# Generic collections
def get_items() -> list[Item]: ...
def get_mapping() -> dict[str, int]: ...

# Callable
from typing import Callable
def apply(fn: Callable[[int], str]) -> str: ...

Pydantic for Validation

When to use Pydantic:
├── API request/response models
├── Configuration/settings
├── Data validation
├── Serialization

Benefits:
├── Runtime validation
├── Auto-generated JSON schema
├── Works with FastAPI natively
└── Clear error messages

Imported: 7. Background Tasks

Selection Guide

When to Use Each

FastAPI BackgroundTasks:
├── Quick operations
├── No persistence needed
├── Fire-and-forget
└── Same process

Celery/ARQ:
├── Long-running tasks
├── Need retry logic
├── Distributed workers
├── Persistent queue
└── Complex workflows

Imported: 10. Decision Checklist

Before implementing:

• Asked user about framework preference?
• Chosen framework for THIS context? (not just default)
• Decided async vs sync?
• Planned type hint strategy?
• Defined project structure?
• Planned error handling?
• Considered background tasks?

Imported: 11. Anti-Patterns to Avoid

❌ DON'T:

• Default to Django for simple APIs (FastAPI may be better)
• Use sync libraries in async code
• Skip type hints for public APIs
• Put business logic in routes/views
• Ignore N+1 queries
• Mix async and sync carelessly

✅ DO:

• Choose framework based on context
• Ask about async requirements
• Use Pydantic for validation
• Separate concerns (routes → services → repos)
• Test critical paths

Remember: Python patterns are about decision-making for YOUR specific context. Don't copy code—think about what serves your application best.

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.