django-recommender-search-backend-patterns

django-recommender-search-backend-patterns is a Backend frameworks skill for AI agents, published by pproenca in the dot-skills repository on GitHub. Skillspace rates django-recommender-search-backend-patterns D (67/100) and it passed the safety scan; the rating is based on static analysis only.

Django backend patterns for recommendation services (AWS Personalize, Databricks Model Serving, internal microservices) and OpenSearch-backed search/feed endpoints. Covers fan-out orchestration (asyncio.gather, deadline propagation, partial results, async client reuse), external service protection (timeouts, circuit breakers, jittered retry, bulkheads, rate limits), OpenSearch query patterns (search_after, _source filtering, function_score, aliases, routing, bool.filter), result blending (score normalization, MMR, dedup, cold-start), Redis caching (stampede protection, model-versioned keys, two-tier, negative), resilience (partial-response envelope, stale-on-error, graceful degradation), async (sync_to_async, async ORM, uvicorn, contextvars, disconnect cancellation), and DRF response shape (cursor pagination, ETag, throttling). Use when building, reviewing, or refactoring such a Django backend. Triggers even without explicit "scale" cues. Includes 5 scaffolding templates.

Grade

D

Quality

67 / 100

Safety

Passed

GitHub stars

180

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