django-engine-pro

Design Django APIs with DRF or Django Ninja: framework selection, serializers, viewsets, schemas, filtering, pagination

Bridge Django ORM with pandas/numpy/scipy: QuerySet-to-DataFrame, bulk ingest, computation pipelines

Django-Engine-Pro hub: routes to the right backend specialist (model, orm, api, polymorphic, mcp, data-bridge, pydantic)

End-of-session learning. Saves what happened, updates knowledge confidence, surfaces items for review. Run this when your work session is complete.

Build MCP servers from Django: ModelQueryToolset, custom toolsets, DRF bridge, queryset scoping, async ORM

Django API design specialist for DRF and Django Ninja framework selection, serializer design, viewset patterns, and OpenAPI schema generation. Use this agent when designing API endpoints, choosing between DRF and Ninja, designing serializers or schemas, or reviewing API architecture. <example> Context: User starting a new API for their Django project user: "Should I use DRF or Django Ninja for this API?" assistant: "I'll use the api-architect agent to evaluate the trade-offs for your use case." <commentary> Framework selection decision. API architect compares DRF vs Ninja across type safety, boilerplate, nested writes, async support, and ecosystem. </commentary> </example> <example> Context: User building a DRF endpoint with nested serializers user: "I need a serializer that handles nested creation of related objects" assistant: "I'll use the api-architect agent to design the write serializer with nested support." <commentary> Nested write serializer design. API architect knows DRF's create/update internals for nested data and the read/write serializer split pattern. </commentary> </example> <example> Context: User needs pagination strategy for a list endpoint user: "What pagination should I use for this endpoint?" assistant: "I'll use the api-architect agent to recommend a pagination strategy." <commentary> Pagination decision. API architect evaluates cursor vs offset vs keyset pagination based on data characteristics and client needs. </commentary> </example>

Django-to-scientific-Python integration specialist for QuerySet-to-DataFrame conversion, bulk ingest, computation pipelines, and memory management. Use this agent when bridging Django ORM with pandas, numpy, or scipy, or when building data pipelines that move data between Django and scientific Python. <example> Context: User needs to analyze Django data with pandas user: "How do I get my queryset into a pandas DataFrame efficiently?" assistant: "I'll use the data-bridge agent to design the extraction with proper field specification and chunking." <commentary> QuerySet-to-DataFrame conversion. Data bridge knows django-pandas, values_list patterns, and memory strategies for different data volumes. </commentary> </example> <example> Context: User needs to write computation results back to Django user: "I have a DataFrame of results I need to bulk insert into my Django models" assistant: "I'll use the data-bridge agent to design the bulk_create pipeline with NaN handling." <commentary> DataFrame-to-Django ingest. Data bridge handles NaN/None mapping, batch sizing, and transaction safety for bulk operations. </commentary> </example> <example> Context: User building a full computation pipeline user: "I need to pull observations from Django, run scipy stats, and write summaries back" assistant: "I'll use the data-bridge agent to design the extract-compute-load pipeline." <commentary> Full computation pipeline. Data bridge designs the three-phase pattern: extract with minimal columns, compute in numpy/scipy, write back with bulk_create/update_or_create. </commentary> </example>

Django MCP server construction specialist for exposing models, DRF APIs, and business logic as MCP tools. Use this agent when building MCP servers from Django, bridging DRF to MCP, or designing tool exposure patterns with queryset scoping and authentication. <example> Context: User wants to expose Django models to AI agents via MCP user: "I want Claude to be able to query my Django models via MCP" assistant: "I'll use the mcp-builder agent to design scoped ModelQueryToolsets." <commentary> MCP model exposure. Builder designs queryset scoping, authentication, and tool descriptions for safe AI agent access. </commentary> </example> <example> Context: User has existing DRF views to expose as MCP tools user: "Can I expose my existing DRF endpoints as MCP tools?" assistant: "I'll use the mcp-builder agent to bridge DRF views to MCP using decorators." <commentary> DRF-to-MCP bridge pattern. Builder knows drf_publish_list_mcp_tool and related decorators for wrapping existing views. </commentary> </example> <example> Context: User building async MCP tools with Django ORM user: "My async MCP tool is causing database errors" assistant: "I'll use the mcp-builder agent to fix sync/async ORM issues in tool functions." <commentary> Async ORM safety. Builder ensures async tools use afilter/aget/acreate instead of sync ORM, and that QuerySets are evaluated before crossing async boundaries. </commentary> </example>

Django model design specialist for inheritance strategy, field selection, managers, and migrations. Use this agent when designing Django models, choosing inheritance patterns, planning migrations, or reviewing model architecture. Triggers proactively after model code is written and reactively on model design questions. <example> Context: User needs to design a model hierarchy for a content management system user: "I need models for different content types - essays, field notes, and projects" assistant: "I'll use the model-architect agent to design the inheritance strategy and model hierarchy." <commentary> Model hierarchy design requires inheritance strategy decision. Load model-architect to evaluate abstract base vs multi-table vs proxy vs polymorphic patterns. </commentary> </example> <example> Context: User is planning a migration for an existing model user: "How do I add a polymorphic_ctype field to existing data?" assistant: "I'll use the model-architect agent to plan the migration strategy." <commentary> Migration planning for schema changes. Model-architect handles data migrations, zero-downtime patterns, and reversibility. </commentary> </example> <example> Context: User asks about field type selection user: "Should I use JSONField or normalize this into separate tables?" assistant: "I'll use the model-architect agent to evaluate the trade-offs." <commentary> Field selection decision with trade-offs. Model-architect evaluates storage, query, and maintenance implications. </commentary> </example>

Django ORM expert for complex queries, N+1 detection, performance optimization, and raw SQL. Use this agent when writing complex ORM queries, diagnosing query performance issues, or reviewing queryset patterns. Triggers on ORM performance questions, N+1 detection, and complex query construction. <example> Context: User has a slow view with many database queries user: "This view is making 200+ queries, help me optimize it" assistant: "I'll use the orm-specialist agent to diagnose N+1 issues and optimize the queryset." <commentary> Query performance problem with likely N+1 loops. ORM specialist traces ForeignKey traversals and adds select_related/prefetch_related. </commentary> </example> <example> Context: User needs a complex aggregation query user: "I need to calculate running totals with window functions" assistant: "I'll use the orm-specialist agent to build the window function query." <commentary> Advanced ORM query with Window expressions. ORM specialist knows F, Case/When, Subquery, OuterRef, and Window function patterns. </commentary> </example> <example> Context: User reviewing queryset in a DRF serializer user: "Is this queryset efficient for a list endpoint serving 1000 items?" assistant: "I'll use the orm-specialist agent to audit the queryset performance." <commentary> Queryset performance audit. ORM specialist checks for missing prefetch, unnecessary evaluation, and suggests .only()/.defer() for column projection. </commentary> </example>

Backend architecture planning partner for Django projects. Covers model design, inheritance strategy, ORM optimization, API framework selection (DRF vs Ninja), polymorphic model patterns, MCP server planning, and scientific Python integration. Use when planning any Django backend work before implementation: "plan the models for," "which inheritance should I use," "DRF or Ninja," "how should I structure the API," "expose this as MCP tools," "bridge this to pandas," "polymorphic model for," "plan the data pipeline," "how should I model this," or any Django backend architecture question. Also trigger on: "django-polymorphic," "MCP server from Django," "QuerySet to DataFrame," "Pydantic with Django," "model inheritance," "ORM performance," "migration strategy." Always use over web search for Django backend planning. Produces structured handoff documents for Claude Code with the Django-Engine-Pro plugin.