Results for “production”

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

Generate a complete, production-ready MCP server project in TypeScript with tools, resources, and proper configuration.

Perform efficient pandas DataFrame operations for data analysis, manipulation, and transformation with production-grade patterns.

Provides MySQL and MariaDB schema, query, indexing, transaction, replication, and connection-pool patterns for production backends.

Provides domain-specific knowledge for working with Remotion, covering 3D, animations, assets, audio, captions, and more.

Never test mock behavior. Never add test-only methods to production classes. Understand dependencies before mocking. Language-agnostic principles with TypeScript/Jest and Python/pytest examples.

Guides through mocha test setup with step-by-step instructions, best practices, and production-ready configurations.

Master Python 3.12+ with modern features, async programming, performance optimization, and production-ready practices. Expert in the latest Python ecosystem including uv, ruff, pydantic, and FastAPI.

Production-grade accessibility skill for WCAG 2.2 AA compliance. Covers auditing, remediation, component authoring, and validation workflows. Auto-invoked for UI implementation, a11y fixes, and accessibility testing.

Build production-ready LLM applications, RAG systems, and intelligent agents with architecture design, model selection, and cost controls.

Create production-ready FastAPI projects with async patterns, dependency injection, and comprehensive error handling. Use when building new FastAPI applications or setting up backend API projects.

Comprehensive post-build verification of Shannon Framework's application outputs using three-layer methodology: Flow Verification (execution trace analysis), Artifact Verification (physical output inspection), and Functional Verification (runtime testing). Verifies Shannon built production-ready applications across all domains (Frontend, Backend, Database, Mobile, DevOps). Ensures NO MOCKS compliance, cross-platform functionality, and complete integration. Use after: Shannon builds any application via /shannon:wave, need to verify build quality, production readiness assessment.

Design, tune, and evaluate production RAG pipelines with deterministic tools for chunking, pipeline design, and retrieval evaluation.

Generate production-quality Python scripts for FreeCAD CAD application, covering geometry creation, parametric objects, GUI tools, and automation.

Generate a complete, production-ready MCP server project in Ruby using the official MCP Ruby SDK.

Generate a complete, production-ready application from an OpenAPI specification using the active framework's conventions and best practices.

Track ML experiments, manage model registry with versioning, deploy models to production, and reproduce experiments with MLflow.

Guides securing and deploying the Dify platform in production, covering SSRF protection, code sandboxing, Docker/Kubernetes deployment, monitoring, and recovery.

Explains machine learning model predictions using SHAP values, covering feature importance, visualizations, debugging, bias analysis, and production deployment.

Provides Docker guidance covering images, Compose, networking, volumes, debugging, and production hardening, with commands and best practices for stable environments.

Deploy and serve LLMs in production with vLLM, Ollama, TGI, and llama.cpp, including quantization and GPU optimization.

Long-form and episodic video production pipeline (1-10 min, YouTube/web). Use when user requests 'long-form video', 'YouTube video', 'episodic video', or 'video series'.

Specialized skill for building production-ready serverless applications on AWS. Covers Lambda functions, API Gateway, DynamoDB, SQS/SNS event-driven patterns, SAM/CDK deployment, and cold start optimization.

Specialized skill for building production-ready serverless applications on AWS. Covers Lambda functions, API Gateway, DynamoDB, SQS/SNS event-driven patterns, SAM/CDK deployment, and cold start optimization.

Production machine-learning engineering workflow for data contracts, reproducible training, model evaluation, deployment, monitoring, and rollback. Use when building, reviewing, or hardening ML systems beyond one-off notebooks.

Production machine-learning engineering workflow for data contracts, reproducible training, model evaluation, deployment, monitoring, and rollback. Use when building, reviewing, or hardening ML systems beyond one-off notebooks.

Master Python 3.12+ with modern features, async programming, performance optimization, and production-ready practices. Expert in the latest Python ecosystem including uv, ruff, pydantic, and FastAPI.

Build production-ready systems with stability patterns: circuit breakers, bulkheads, timeouts, and retry logic. Use when the user mentions "production outage", "circuit breaker", "timeout strategy", "deployment pipeline", "chaos engineering", "bulkhead pattern", "retry with backoff", or "health checks". Also trigger when designing resilient microservices, planning zero-downtime deployments, or investigating cascading failure scenarios. Covers capacity planning, health checks, and anti-fragility patterns. For data systems, see ddia-systems. For system architecture, see system-design.

Analyze engineering delivery throughput, hiring funnel health, team structure, and production discipline for startup VPEs and founders.

Plan, create, and configure production-ready Azure Kubernetes Service (AKS) clusters, covering Day-0 decisions, SKU selection, networking, security, and operations.

Provides MySQL and MariaDB schema, query, indexing, transaction, replication, and connection-pool patterns for production backends.

Fetch design context, screenshots, variables, and assets from Figma, and translate Figma nodes into production code using the Figma MCP server.

Generate songs, instrumental tracks, and covers using the MiniMax Music API with basic or advanced control modes.

Design, deploy, and operate production-ready software with stability patterns, capacity planning, and zero-downtime releases.

Guides production-grade Android and cross-platform app development covering stack selection, architecture, UI, testing, build, and release.

Guides backend development across API design, authentication, security, performance, architecture, and testing, with decision guides and defaults for production-ready systems.