Results for “app-deployment”

Secure Laravel applications with best practices for authentication, authorization, Eloquent safety, CSRF, XSS prevention, API security, and deployment configurations.

Set up and initialize AWS CDK applications in Python, including prerequisites, project creation, dependency management, and deployment.

Develop Nuxt 3 full-stack Vue applications with SSR, file-based routing, auto-imports, data fetching, and deployment guidance.

Coordinate end-to-end deployments across Stripe, Supabase, and Vercel using the Composio CLI. Update pricing, apply database migrations, deploy frontend, verify changes, and announce releases from a single script.

Build scalable Django 5.x applications with async views, DRF, Celery, and Channels, following best practices for architecture, testing, and deployment.

Build, preview, and deploy websites, web apps, and APIs using Vercel, Railway, GitHub Pages, or local Canvas environments. Use when a user needs general web deployment guidance across common hosting targets.

Reference for building real-time voice and multimodal AI applications with Pipecat, covering pipelines, speech services, LLMs, transports, and deployment.

Use this when reviewing whether an app, service, worker, CLI, or serverless system is ready for its target environment with respect to config, secrets, migrations, dependencies, observability, rollback, and runtime constraints.

Builds, tests, and deploys Microsoft 365 apps and agents for Teams and Copilot using the ATK CLI, with sub-skills for project creation, local testing, cloud deployment, troubleshooting, and Slack-to-Teams migration.

Expert knowledge for Azure App Service development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when choosing App Service plans/ASE, configuring VNet/custom domains/certs, CI/CD slots, or managed identity access, and other Azure App Service related development tasks. Not for Azure Functions (use azure-functions), Azure Spring Apps (use azure-spring-apps), Azure Static Web Apps (use azure-static-web-apps), Azure Service Fabric (use azure-service-fabric).

Use for writing, reviewing, debugging, or testing Python Streamlit apps, especially rerun behavior, widget identity and callbacks, session_state, cache_data/cache_resource, forms, fragments, containers, multipage navigation, uploads/downloads, and Streamlit-hosted chart/table interaction. Do not use for standalone Plotly/Altair figure design, generic backend services, Dash apps, or deployment configuration without app code.

Operates Laravel applications in production, covering Horizon, queues, scheduler, Octane, Redis, Supervisor, PHP-FPM, cache, and deployment with safety gates for destructive commands.

Master Django 5.x with async views, DRF, Celery, and Django Channels. Build scalable web applications with proper architecture, testing, and deployment. Use PROACTIVELY for Django development, ORM optimization, or complex Django patterns.

Expert knowledge for Azure Functions development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when building trigger/binding workflows, Durable Functions, containerized apps, Flex/Consumption plans, or CI/CD deploys, and other Azure Functions related development tasks. Not for Azure App Service (use azure-app-service), Azure Logic Apps (use azure-logic-apps), Azure Container Apps (use azure-container-apps), Azure Kubernetes Service (AKS) (use azure-kubernetes-service).

Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.

Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.

Deploy static sites and full-stack apps to PinMe (IPFS-based hosting). One-command upload of HTML/directories, or full-stack project creation with frontend+backend+database. Authentication required — no anonymous upload path exists.

Audit Solidity smart contracts for reentrancy, oracle manipulation, token decimals, SafeERC20, ERC-4626 inflation, infinite approvals, and MEV vulnerabilities with a pre-deployment checklist.

Hardens Laravel applications against common vulnerabilities with guidance on authentication, authorization, validation, CSRF, mass assignment, file uploads, secrets, rate limiting, and secure deployment.

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.

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

Expert skill for building LLM applications with the LlamaIndex framework — RAG pipelines, multi-agent orchestration, event-driven workflows, knowledge graph construction, production deployment, and evaluation. Use when working with LlamaIndex or comparing RAG and agent orchestration frameworks.

Plan and execute Azure deployments safely and repeatably for applications and infrastructure. Use this skill when users ask to deploy, publish, release, or provision workloads in Azure. Covers Infrastructure-as-Code, idempotency, health validation, and rollback strategies.

Deploys applications to a self-hosted Platform-as-a-Service using Dokku, supporting buildpacks, Dockerfiles, custom domains, and SSL via Let's Encrypt.

Upgrades the local StaticFlow backend behind a running Pingora gateway on port 39180 using blue/green slots, applying config changes via SIGHUP without restarting the gateway.

You are an expert in Bolt.new by StackBlitz, the AI-powered full-stack development environment that runs entirely in the browser. You help developers go from idea to deployed app in minutes using natural language prompts — Bolt generates complete applications with frontend, backend, database, and deployment, all running in a WebContainer without local setup.

Build applications that programmatically interact with GitHub Copilot using the Copilot SDK, supporting session management, custom tools, streaming, hooks, MCP servers, and deployment across Node.js, Python, Go, and .NET.

Run LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.

Expert in Expo for React Native development. Covers Expo Router, EAS Build and Submit, development builds, native module integration, and production deployment. Knows how to build cross-platform mobile apps efficiently while maintaining access to native capabilities. Use when "expo, react native, mobile app, eas build, expo router, ios android, expo, react-native, mobile, ios, android, eas, cross-platform" mentioned.

Expert knowledge for Azure AI Bot Service development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when building Azure AI bots with Direct Line/Web Chat, Teams, OAuth/SSO, human handoff, or proactive messaging, and other Azure AI Bot Service related development tasks. Not for Azure Health Bot (use azure-health-bot), Azure Functions (use azure-functions), Azure App Service (use azure-app-service), Azure Logic Apps (use azure-logic-apps).

Expert knowledge for Azure Front Door development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when configuring Front Door routing/caching, rules engine, TLS/origin auth, CDN video delivery, or POP limits, and other Azure Front Door related development tasks. Not for Azure Application Gateway (use azure-application-gateway), Azure Traffic Manager (use azure-traffic-manager), Azure Load Balancer (use azure-load-balancer), Azure Web Application Firewall (use azure-web-application-firewall).

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`.

Expert knowledge for Azure Logic Apps development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when building Logic Apps with SAP/IBM/FTP connectors, B2B/EDI flows, AI/OpenAI calls, XML/JSON transforms, or DevOps CI/CD, and other Azure Logic Apps related development tasks. Not for Azure Functions (use azure-functions), Azure API Management (use azure-api-management), Azure Service Bus (use azure-service-bus), Azure Event Grid (use azure-event-grid).

Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.

Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.

Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.