Results for “production”

Pixel-perfect Figma to React conversion using coderio. Generates production-ready code (TypeScript, Vite, TailwindCSS V4) with high visual fidelity. Features robust error handling, checkpoint recovery, and streamlined execution via helper script.

Expert in LangGraph - the production-grade framework for building stateful, multi-actor AI applications. Covers graph construction, state management, cycles and branches, persistence with checkpointers, human-in-the-loop patterns, and the ReAct agent pattern.

Build, review, and refactor Java backend services (Spring Boot). Use for tasks like REST API design, controllers/services/repositories, PostgreSQL persistence (JPA/MyBatis), migrations, configuration, security (Spring Security), observability, testing, performance tuning, and production hardening.

Apply opinionated, production-grade Python design and review standards. Use for substantial Python implementation, refactoring, API design, module design, exception boundaries, interfaces, or a deep code-quality review; not for a quick standalone script.

Knowledge-graph-augmented retrieval. Entity and triple extraction, graph construction (Neo4j, LlamaIndex PropertyGraphIndex), hierarchical community summarization (Microsoft GraphRAG), personalized PageRank (HippoRAG), multi-hop traversal retrieval, and hybrid graph + vector pipelines. USE WHEN: user mentions "GraphRAG", "HippoRAG", "knowledge graph RAG", "entity extraction", "multi-hop reasoning", "Neo4j RAG", "LlamaIndex property graph", "LangChain graph retriever", "triple extraction", "community summarization" DO NOT USE FOR: vanilla vector RAG - use `rag-patterns`; multimodal inputs - use `multimodal-rag`; production indexing ops - use `rag-production`; hallucination checks - use `rag-guardrails`

Design and ship production-ready MCP servers from OpenAPI contracts instead of hand-written tool wrappers. Supports Python and TypeScript, schema validation, and safe evolution.

Provides production-grade Django architecture patterns including project structure, model design, REST API design with DRF, ORM best practices, caching, signals, and middleware.

Provides production-ready Dart and Flutter code patterns for null safety, immutable state, async composition, widget architecture, state management, navigation, networking, and testing.

Provides guidance and code for production AI workflows including RAG pipelines, vector databases, embedding indexing, prompt engineering, cost estimation, semantic caching, and quality evaluation.

Guides developers through capturing diagnostic artifacts to diagnose production .NET performance issues, including tool selection and data collection across Windows, Linux, containers, and Kubernetes.

Generate a complete, production-ready Model Context Protocol server project in Java using the official MCP Java SDK with Maven or Gradle.

Build, test, lint, version, and publish production-grade Python libraries to PyPI with support for multiple build backends, versioning strategies, and CI/CD.

Provides structured guidance for building production-grade AI agent systems through context engineering, covering fundamentals, architectural patterns, operational excellence, and evaluation.

Creates SageMaker endpoints (real-time or async) with autoscaling, CloudWatch alarms, and tagging enabled by default, then smoke-tests them before declaring success.

Enforces production-grade Python and AI engineering standards for FastAPI, LangChain/LangGraph, RAG pipelines, and LLM integrations, covering type safety, error handling, testing, and security.

Builds multi-tenant Laravel SaaS applications with stancl/tenancy v3, covering tenant identification, data isolation, bootstrappers, queues, and production patterns.

Provides production-grade Django architecture patterns covering project structure, split settings, ORM best practices, REST API design with DRF, caching, signals, and middleware.

Creates production-grade p5.js sketches: generative art, interactive visualizations, 3D scenes, shaders, and audio-reactive visuals, with export to HTML, PNG, GIF, MP4, or SVG.

Generates on-brand interfaces and assets for Sprintable, from throwaway HTML prototypes to production code, using the included design tokens, fonts, and UI kit components.

Writes production-quality Pine Script v6 code following TradingView guidelines and best practices. Use when implementing indicators, strategies, or any Pine Script code. Triggers on requests to create, write, implement, or code Pine Script functionality.

Use this skill for brand direction, color systems, typography, tone, reusable visual themes. Trigger when the task involves web design work related to Brand Theme Factory, production implementation, audits, debugging, strategy, or validation.

Use this skill for information architecture, internal links, hub pages, anchor text, crawl depth. Trigger when the task involves seo work related to Internal Linking Ia, production implementation, audits, debugging, strategy, or validation.

Use this skill for motion tokens, durations, easing, choreographed transitions, reduced-motion support. Trigger when the task involves ui/ux work related to Motion Design System, production implementation, audits, debugging, strategy, or validation.

Use this skill for ASO, app metadata, screenshots, descriptions, keywords, review positioning. Trigger when the task involves apps work related to App Store Aso Metadata, production implementation, audits, debugging, strategy, or validation.

Use this skill for custom shaders, materials, postprocessing, visual effects, uniforms, GLSL patterns. Trigger when the task involves 3d web work related to Shader Material Design, production implementation, audits, debugging, strategy, or validation.

High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.

High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.

Expert AI/ML engineer specializing in machine learning model development, deployment, and integration into production systems. Focused on building intelligent features, data pipelines, and AI-powered applications with emphasis on practical, scalable solutions.

Plan new YouTube videos with optimized titles, thumbnails, and hooks. Use when planning a video, writing or reviewing opening hooks, generating title and thumbnail concepts, or creating a production-ready video plan backed by research.

High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.

Use this when the project needs a clear strategy for local, test, staging, production, cloud, serverless, or hybrid environments. Trigger on requests about environment layout, runtime differences, environment-specific behavior, or deployment-context planning.

High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.

Provides production-grade FastAPI development patterns including project structure, Pydantic v2 schemas, dependency injection, async handlers, authentication, authorization, transactional service layers, and testing with httpx and pytest.

Build production-ready React Native and Expo apps with guidance on components, styling, animations, navigation, state management, forms, networking, performance, testing, and deployment.

Facilitates discussion-based tabletop exercises for SOC teams to test incident response procedures, communication workflows, and decision-making under pressure without impacting production systems.

Designs and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines, applying reranking, and evaluating retrieval quality.