django-drf▌
prowler-cloud/prowler · updated Apr 8, 2026
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Note: swagger_fake_view is specific to drf-spectacular for OpenAPI schema generation.
Critical Patterns
- ALWAYS separate serializers by operation: Read / Create / Update / Include
- ALWAYS use
filterset_classfor complex filtering (notfilterset_fields) - ALWAYS validate unknown fields in write serializers (inherit
BaseWriteSerializer) - ALWAYS use
select_related/prefetch_relatedinget_queryset()to avoid N+1 - ALWAYS handle
swagger_fake_viewinget_queryset()for schema generation - ALWAYS use
@extend_schema_fieldfor OpenAPI docs onSerializerMethodField - NEVER put business logic in serializers - use services/utils
- NEVER use auto-increment PKs - use UUIDv4 or UUIDv7
- NEVER use trailing slashes in URLs (
trailing_slash=False)
Note:
swagger_fake_viewis specific to drf-spectacular for OpenAPI schema generation.
Implementation Checklist
When implementing a new endpoint, review these patterns in order:
| # | Pattern | Reference | Key Points |
|---|---|---|---|
| 1 | Models | api/models.py |
UUID PK, inserted_at/updated_at, JSONAPIMeta.resource_name |
| 2 | ViewSets | api/base_views.py, api/v1/views.py |
Inherit BaseRLSViewSet, get_queryset() with N+1 prevention |
| 3 | Serializers | api/v1/serializers.py |
Separate Read/Create/Update/Include, inherit BaseWriteSerializer |
| 4 | Filters | api/filters.py |
Use filterset_class, inherit base filter classes |
| 5 | Permissions | api/base_views.py |
required_permissions, set_required_permissions() |
| 6 | Pagination | api/pagination.py |
Custom pagination class if needed |
| 7 | URL Routing | api/v1/urls.py |
trailing_slash=False, kebab-case paths |
| 8 | OpenAPI Schema | api/v1/views.py |
@extend_schema_view with drf-spectacular |
| 9 | Tests | api/tests/test_views.py |
JSON:API content type, fixture patterns |
Full file paths: See references/file-locations.md
Decision Trees
Which Serializer?
GET list/retrieve → <Model>Serializer
POST create → <Model>CreateSerializer
PATCH update → <Model>UpdateSerializer
?include=... → <Model>IncludeSerializer
Which Base Serializer?
Read-only serializer → BaseModelSerializerV1
Create with tenant_id → RLSSerializer + BaseWriteSerializer (auto-injects tenant_id on create)
Update with validation → BaseWriteSerializer (tenant_id already exists on object)
Non-model data → BaseSerializerV1
Which Filter Base?
Direct FK to Provider → BaseProviderFilter
FK via Scan → BaseScanProviderFilter
No provider relation → FilterSet
Which Base ViewSet?
RLS-protected model → BaseRLSViewSet (most common)
Tenant operations → BaseTenantViewset
User operations → BaseUserViewset
No RLS required → BaseViewSet (rare)
Resource Name Format?
Single word model → plural lowercase (Provider → providers)
Multi-word model → plural lowercase kebab (ProviderGroup → provider-groups)
Through/join model → parent-child pattern (UserRoleRelationship → user-roles)
Aggregation/overview → descriptive kebab plural (ComplianceOverview → compliance-overviews)
Serializer Patterns
Base Class Hierarchy
# Read serializer (most common)
class ProviderSerializer(RLSSerializer):
class Meta:
model = Provider
fields = ["id", "provider", "uid", "alias", "connected", "inserted_at"]
# Write serializer (validates unknown fields)
class ProviderCreateSerializer(RLSSerializer, BaseWriteSerializer):
class Meta:
model = Provider
fields = ["provider", "uid", "alias"]
# Include serializer (sparse fields for ?include=)
class ProviderIncludeSerializer(RLSSerializer):
class Meta:
model = Provider
fields = ["id", "alias"] # Minimal fields
SerializerMethodField with OpenAPI
from drf_spectacular.utils import extend_schema_field
class ProviderSerializer(RLSSerializer):
connection = serializers.SerializerMethodField(read_only=True)
@extend_schema_field({
"type": "object",
"properties": {
"connected": {"type": "boolean"},
"last_checked_at": {"type": "string", "format": "date-time"},
},
})
def get_connection(self, obj):
return {
"connected": obj.connected,
"last_checked_at": obj.connection_last_checked_at,
}
Included Serializers (JSON:API)
class ScanSerializer(RLSSerializer):
included_serializers = {
"provider": "api.v1.serializers.ProviderIncludeSerializer",
}
Sensitive Data Masking
def to_representation(self, instance):
data = super().to_representation(instance)
# Mask by default, expose only on explicit request
fields_param = self.context.get("request").query_params.get("fields[my-model]", "")
if "api_key" in fields_param:
data["api_key"] = instance.api_key_decoded
else:
data["api_key"] = "****" if instance.api_key else None
return data
ViewSet Patterns
get_queryset() with N+1 Prevention
Always combine swagger_fake_view check with select_related/prefetch_related:
def get_queryset(self):
# REQUIRED: Return empty queryset for OpenAPI schema generation
if getattr(self, "swagger_fake_view", False):
return Provider.objects.none()
# N+1 prevention: eager load relationships
return Provider.objects.select_related(
"tenant",
).prefetch_related(
"provider_groups",
Prefetch("tags", queryset=ProviderTag.objects.filter(tenant_id=self.request.tenant_id)),
)
Why swagger_fake_view? drf-spectacular introspects ViewSets to generate OpenAPI schemas. Without this check, it executes real queries and can fail without request context.
Action-Specific Serializers
def get_serializer_class(self):
if self.action == "create":
return ProviderCreateSerializer
elif self.action == "partial_update":
return ProviderUpdateSerializer
elif self.action in ["connection", "destroy"]:
return TaskSerializer
return ProviderSerializer
Dynamic Permissions per Action
class ProviderViewSet(BaseRLSViewSet):
required_permissions = [Permissions.MANAGE_PROVIDERS]
def set_required_permissions(self):
if self.action in ["list", "retrieve"]:
self.required_permissions = [] # Read-only = no permission
else:
self.required_permissions = [Permissions.MANAGE_PROVIDERS]
Cache Decorator
from django.utils.decorators import method_decorator
from django.views.decorators.cache import cache_control
CACHE_DECORATOR = cache_control(
max_age=django_settings.CACHE_MAX_AGE,
stale_while_revalidate=django_settings.CACHE_STALE_WHILE_REVALIDATE,
How to use django-drf on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add django-drf
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches django-drf from GitHub repository prowler-cloud/prowler and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate django-drf. Access the skill through slash commands (e.g., /django-drf) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★54 reviews- ★★★★★Benjamin Yang· Dec 24, 2024
Solid pick for teams standardizing on skills: django-drf is focused, and the summary matches what you get after install.
- ★★★★★Sakura Smith· Dec 24, 2024
We added django-drf from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Pratham Ware· Dec 16, 2024
django-drf is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Hiroshi Flores· Dec 12, 2024
django-drf has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Amina Ndlovu· Dec 4, 2024
django-drf reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Benjamin Lopez· Nov 15, 2024
Registry listing for django-drf matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Sofia Robinson· Nov 15, 2024
django-drf fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Arjun Martin· Nov 11, 2024
I recommend django-drf for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Yash Thakker· Nov 7, 2024
Keeps context tight: django-drf is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Arjun Dixit· Nov 3, 2024
Useful defaults in django-drf — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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