Verify Django

Skill: Verify Django

Purpose

Systematic verification checklist for Django/DRF code. Used by the reviewer agent in a separate Claude session after code execution.

Verification Process

Run through each section. Flag issues as PASS, WARN, or FAIL.

1. Tests

• Tests exist for new/changed code
• pytest passes with no failures
• Test coverage is reasonable (not just happy path)
• Factory data is realistic (not "test123" placeholder values)

2. Models

• Migrations are generated and match model changes
• No migration conflicts with main branch
• New fields have sensible defaults or are nullable where appropriate
• Indexes exist on fields used in filters/lookups
• __str__ methods are defined

3. Serializers

• Fields are explicitly listed (no __all__)
• Read vs write serializers separated where needed
• Validation covers edge cases (empty strings, nulls, boundaries)
• Nested serializer performance — no N+1 from nested reads

4. Views

• permission_classes set on every view
• Querysets use select_related/prefetch_related appropriately
• Pagination is configured for list endpoints
• Error responses are consistent and informative
• No business logic in views — delegated to models/services

5. Security

• Authentication required on all non-public endpoints
• Object-level permissions checked (not just role-based)
• SECRET_KEY is set from the environment and not the shipped default (django-insecure-change-me-in-production) in production
• No sensitive data in response bodies that shouldn't be there
• Input validation prevents injection (Django handles most, but check raw SQL)

6. API Contract

• Endpoint paths follow project conventions
• Response shapes are consistent with existing endpoints
• Integration summary is up to date
• Breaking changes are documented

7. Code Quality

• No commented-out code
• No debug prints or leftover logging
• Imports are clean (no unused)
• Linting passes (ruff/flake8)
• Type hints on function signatures

8. Development Environment

• Dockerfile.dev exists
• docker-compose.yml has the services for the project's blueprint tier — minimal: web + db; standard: + redis; full: + celery + celery-beat (check which tier the project was bootstrapped with)
• Makefile exists and wraps all commands via docker compose exec
• No python manage.py commands used directly on host

Output

Produce a verification-report.md:

# Verification Report: [Feature/Phase]
Date: [date]
Reviewer: Claude (automated)

## Summary: [PASS/WARN/FAIL]

## Details
### Tests: [PASS/WARN/FAIL]
- [notes]

### Models: [PASS/WARN/FAIL]
- [notes]

...

## Issues Found
1. [FAIL] Description + suggested fix
2. [WARN] Description + recommendation

## Recommended Actions
- [ ] Fix: ...
- [ ] Consider: ...