Results for “dep”

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

Deploy and configure Rapid7 InsightVM Security Console and Scan Engines for authenticated and unauthenticated vulnerability scanning across enterprise environments.

Quantize large language models to 4-bit using activation-aware weight quantization, achieving ~3x speedup with minimal accuracy loss for deployment on limited GPU memory.

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

Coordinates a squad of specialized agents for software development, routing tasks and managing context across requirements, planning, architecture, implementation, review, testing, and deployment phases.

Provides patterns and strategies for migrating imperative TypeScript code to fp-ts functional programming patterns, covering error handling, null checks, callbacks, dependency injection, and loops.

Splits oversized pull requests into sequential, reviewable chunks under 300 lines using stacked PRs or feature branch chains, with dependency tracking and review budget management.

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

Guides users through optimizing personal and business banking: choosing accounts, cutting fees, boosting interest, resolving disputes, and structuring finances.

Diagnoses and operates Kubernetes clusters: pods, deployments, ingress, HPA, PVC, StorageClass, NetworkPolicy, Secrets, Helm, ArgoCD, cert-manager. Reads before changing; mutations require confirmation and diff review.

Discovers and lists all URLs on a website without extracting content, using the Tavily CLI. Faster than crawling, with options for depth, filtering, and natural language instructions.

Search the web with LLM-optimized results via the Tavily CLI, returning relevant snippets, relevance scores, and metadata. Supports domain filtering, time ranges, and multiple search depths.

Provides a reference for building real-time voice and multimodal AI agents with Pipecat, covering pipeline architecture, speech services, LLM integration, transports, and deployment.

Guides creating named subset types of WaveEnv to document a component tree's environmental dependencies and enable mock environments for preview/test servers.

Automates ClickUp task, list, folder, and space management via the REST API v2, including time tracking, goals, custom fields, dependencies, and webhooks.

Manages Ray clusters, jobs, Serve deployments, and distributed workloads via the Dashboard API and CLI, with discovery-first checks and safety guardrails.

Enforces GDPR-compliant deployment practices for Google Cloud Run, including Frankfurt region verification, PII-safe logging, data anonymization, retention policies, and CSP configuration.

Debug running Docker containers and Compose services. Use when inspecting container logs, exec-ing into running containers, diagnosing networking issues, checking resource usage, debugging multi-stage builds, troubleshooting health checks, or fixing Compose service dependencies.

Cloud GPU processing via RunPod serverless. Use when setting up RunPod endpoints, deploying Docker images, managing GPU resources, troubleshooting endpoint issues, or understanding costs. Covers all 5 toolkit images (qwen-edit, realesrgan, propainter, sadtalker, qwen3-tts).

.NET backend conventions, patterns, and test infrastructure. Use when working with C# files, .csproj projects, ASP.NET APIs, xUnit tests, FluentAssertions, Entity Framework, WebApplicationFactory, dotnet CLI, NuGet packages, .NET dependency injection, middleware, or controller patterns.

Angular frontend conventions, patterns, and test infrastructure. Use when working with Angular components, services, modules, RxJS, Angular CLI, ng commands, Jasmine tests, Component Harnesses, HttpTestingController, Angular routing, Angular forms, Angular pipes, or Angular dependency injection.

Build production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks. Implements model serving, feature engineering, A/B testing, and monitoring. Use PROACTIVELY for ML model deployment, inference optimization, or production ML infrastructure.

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.

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.

Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.

Cloud GPU processing via RunPod serverless. Use when setting up RunPod endpoints, deploying Docker images, managing GPU resources, troubleshooting endpoint issues, or understanding costs. Covers all 5 toolkit images (qwen-edit, realesrgan, propainter, sadtalker, qwen3-tts).

Open Neural Network Exchange format for model interoperability across frameworks. Export models from PyTorch, TensorFlow, and other frameworks to ONNX, optimize with ONNX Runtime, and deploy for cross-platform inference on CPU, GPU, and edge devices.

Use when the team needs a top-level system architecture spanning frontend, backend, auth, data stores, jobs, integrations, and deployment boundaries. Trigger on requests to design the overall system or validate a proposed architecture.

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.

Prepares production launches. Use when preparing to deploy to production. Use when you need a pre-launch checklist, when setting up monitoring, when planning a staged rollout, or when you need a rollback strategy.

Expert knowledge for SAP HANA on Azure Large Instances development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when deploying HANA Large Instances, S/4HANA or NetWeaver, Azure Monitor for SAP, HA/DR clusters, or RISE links, and other SAP HANA on Azure Large Instances related development tasks. Not for Azure Large Instances (use azure-large-instances), Azure Virtual Machines (use azure-virtual-machines), SQL Server on Azure Virtual Machines (use azure-sql-virtual-machines).

Provides guidance on using Bun as a JavaScript runtime, package manager, bundler, and test runner, including migration from Node.js and Vercel deployment.

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.

Canonical patterns for writing custom MSBuild targets, covering the three-level target chain, chain extension, DependsOnTargets vs BeforeTargets vs AfterTargets, Returns vs Outputs, and common pitfalls.

Design and create AEM Workflow models on AEM 6.5 LTS, covering step types, transitions, variables, and XML deployment via Package Manager or Maven.

Registers prompt-based AI agents in Azure AI Foundry via REST API from a local JSON manifest, making them immediately available for invocation.