Encode Ccres Database

Designs and implements backend systems including REST APIs, microservices, database architectures, authentication flows, and security hardening. Use when the user asks to "design REST APIs", "optimize database queries", "implement authentication", "build microservices", "review backend code", "set up GraphQL", "handle database migrations", or "load test APIs". Covers Node.js/Express/Fastify development, PostgreSQL optimization, API security, and backend architecture patterns.

Develop lightweight Flask APIs with routing, blueprints, database integration, authentication, and request/response handling. Use when building RESTful APIs, microservices, or lightweight web services with Flask.

Proactively apply when creating APIs, backends, or data models. Triggers on PostgreSQL, Postgres, Drizzle, drizzle-orm, drizzle-kit, database, schema, pgTable, tables, columns, indexes, queries, migrations, ORM, relations, relational queries, joins, transactions, SQL, connection pooling, PgBouncer, N+1, JSONB, RLS, full-text search, partitioning. Use when writing database schemas, queries, migrations, connection setup, or any database-related code. PostgreSQL and Drizzle ORM best practices.

Use this skill whenever the user wants to integrate Loops from application code, backend services, webhook handlers, or server-side automation. This includes the Loops HTTP API and official SDKs for server-side contact, contact-property, mailing-list, event, API-key-validation, transactional-email, content editing (campaigns, campaign groups, transactional groups, audience segments, email messages, themes, and components), and workflow graph/node inspection and mutation. Trigger on phrases like "Loops API", "Loops SDK", "create a campaign via API", "update email message LMX", "create a workflow via API", "add a workflow node", "send a Loops event from my app", "add a contact to Loops in a webhook", "send a transactional email from backend code", or any time the user wants to integrate Loops into their app, backend, webhook, or automation. Do not trigger for CLI or shell-only requests.

Redis LangCache guidance for semantic caching of LLM responses on Redis Cloud — calling search/set via the SDK or REST API, tuning the similarity threshold, separating caches per task type, and filtering with custom attributes. Use when caching LLM completions or RAG answers to cut API cost and latency, building a cache-aside layer in front of OpenAI / Anthropic / etc., tuning hit rate vs precision, or splitting one app's LLM workloads into multiple LangCache caches.

Build or review Langfuse backend code. Use for tRPC routers, public REST APIs, BullMQ processors, services, middleware, Prisma or ClickHouse access, OpenTelemetry, Zod, environment configuration, or backend tests.