Plugins

6 plugins

Full-Stack Backend Platforms

For developers building backends with Convex, Firebase, or Django, covering schema design, real-time features, and deployment.

10 skills · plugin

Backend Framework Patterns

For developers building production-grade backends with NestJS, Spring Boot, or Ktor, covering architecture patterns and best practices.

7 skills · plugin

FP-TS Backend Toolkit

For Node.js/Deno developers building type-safe backends with fp-ts, covering async pipelines and service composition.

3 skills · plugin

Secure Firebase Backend

Installs a pipeline to validate, plan, execute, and enforce Firebase security best practices.

7 skills · plugin

Build RAG Pipeline with Pinecone

Build a production RAG pipeline and persistent agent memory using Pinecone as the vector database backend.

6 skills · plugin

@alirezarezvani

Engineering Team

32 engineering skills: architecture, frontend, backend, fullstack, QA, DevOps, security, AI/ML, data engineering, Playwright (9 sub-skills), self-improving agent, Stripe integration, TDD guide, tech stack evaluator, Google Workspace CLI, a11y audit (WCAG 2.2), Azure cloud architect, GCP cloud architect, security pen testing, Snowflake development, adversarial-reviewer, ai-security, cloud-security,

16 skills · plugin

Results for “backend”

38 skills

Route Firebase AI feature work into either direct app/client Firebase AI Logic SDK integration or a server-owned Genkit workflow. Use when a web, mobile, backend, or full-stack feature needs model calls, typed outputs, reusable flows, tools, retrieval, prompt files, evals, observability, or deployment. Choose client-ai-logic, flow-foundation, tool-and-agent, retrieval-and-prompt, evaluation-and-observability, deployment-runtime, or comparison-or-fallback; route Firebase platform/operator work to `firebase-cli` and broad framework comparisons to `survey`.

Agent Observability

Instrument a shipped product's AI agents with tracing and observability so you can see what they did, why outputs happened, and what each run cost. Plain-language primer plus free-tier-first backend selection (Langfuse, Phoenix, LangSmith, Braintrust) and OpenTelemetry/OpenInference instrumentation. Load when the user asks to add observability, add tracing, instrument my agents, see what my agent is doing in production, set up Langfuse or Phoenix or LangSmith, debug why my agent gave a bad answer, or track LLM cost per request. Also fires when agent-system-architecture or setup-evaluation requires an observability plan for an agent-chain product. NOT for tracing the coding agent itself — that is run-trace. Precondition for runtime-learning-loop.