Results for “container-runtime”

Analyzes container security posture, image assurance, compliance, and runtime alerts on the Aqua Security platform via its API.

Detect container escape attempts using Falco, seccomp, and auditd, with rules for privileged containers, Docker socket access, and kernel module loading.

Containerizes applications and deploys them to Google Kubernetes Engine (GKE) with Dockerfiles, manifests, and best practices.

Kubernetes

Audits Kubernetes pods for container escape vectors by analyzing privileged mode, dangerous capabilities, host namespace sharing, and writable hostPath mounts using the Kubernetes Python client.

Monitors cloud workloads for runtime threats by checking process lists, network connections, file integrity, and resource utilization anomalies on EC2 and GCE instances.

Evaluates a containerized framework for deploying distributed big data workloads, measuring execution time and cloud cost scaling from four to eight nodes.

Provides expertise in containerization technology, covering container creation, orchestration integration, security hardening, and operational best practices for production workloads.

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

Specialized skill for building production-ready serverless applications on GCP. Covers Cloud Run services (containerized), Cloud Run Functions (event-driven), cold start optimization, and event-driven architecture with Pub/Sub.

Authorized security assessment for cloud, container, and Kubernetes environments covering metadata SSRF, IAM misconfigurations, container escape paths, and cluster RBAC review.

Specialized skill for building production-ready serverless applications on GCP. Covers Cloud Run services (containerized), Cloud Run Functions (event-driven), cold start optimization, and event-driven architecture with Pub/Sub.

Author or debug Kubernetes manifests with distinct probes, right-sized resources, autoscaling, and safe reversible rollouts.

Manages Google Kubernetes Engine clusters via gcloud CLI, covering cluster discovery, node pool management, workload analysis, autopilot configuration, and upgrade planning.

Inspect containers, logs, and images using podman on Bazzite/Fedora systems.

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.

Exploit privileged pods, host mounts, runC CVEs, and exposed Docker sockets to break out of a container and reach the underlying host during authorized container-security assessments.

Adopt Bun as a JavaScript runtime, package manager, bundler, and test runner, including migration from Node and Vercel deployment.

Docker

Audit CI/CD pipelines (GitHub Actions) for cost, speed, and safety. Use when the Actions bill is high, Actions minutes, runner cost, slow CI, artifact/cache storage, or "CI/CD / workflow audit". Gate logic (bypass, ratchet gaming, required-but-not) → audit-gate-logic.

Guides containerizing apps with Docker Compose, optimizing Dockerfiles, and deploying to Kubernetes, with a focus on turning these tasks into a paid service.

Run a command inside a running Docker container

Start a stopped Docker container by name or ID

Apply CIS Docker Benchmark v1.8.0 security best practices to harden Docker containers for production, covering daemon configuration, image building, runtime controls, and auditing.

Google Cloud Platform SDK integration. Cloud Functions, Firestore, Cloud Storage, Pub/Sub, BigQuery, and Cloud Run. Node.js and Python client libraries. USE WHEN: user mentions "GCP", "Google Cloud", "Cloud Functions", "Firestore", "Cloud Storage", "Pub/Sub", "BigQuery", "Cloud Run", "Firebase" DO NOT USE FOR: AWS services - use `aws`; Azure services - use `azure`; Firebase Auth - use auth skills

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.

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.

Run a full pipeline for a task. Orchestrates roles through stages (standalone or HOTL-integrated).

Run TAO SDK jobs as Docker containers on a local or remote Docker daemon with NVIDIA GPU support, including preflight checks and credential handling.

Deploys and manages Falco YAML rules for runtime threat detection in containers and Kubernetes, monitoring syscalls for shell spawns, file tampering, network anomalies, and privilege escalation. Parses Falco alerts for incident response.