Results for “ginkgo-cloud-lab”

Manage and use Lambda Labs GPU cloud instances for ML training and inference with SSH access, persistent filesystems, and multi-node clusters.

Protects stateful GKE workloads by configuring backup plans, restore workflows, and disaster recovery using Backup for GKE.

Reserved and on-demand GPU cloud instances for ML training and inference. Use when you need dedicated GPU instances with simple SSH access, persistent filesystems, or high-performance multi-node clusters for large-scale training.

Manages project lifecycle tasks from a single CLI: initialize, check, build, test, deploy guidance, config, templates, docs, and cleanup.

Operates Google Cloud Platform resources via the gcloud CLI, covering compute instances, storage buckets, BigQuery, Cloud Run, GKE, IAM, and billing. It checks the environment, authenticates, confirms destructive operations, and diagnoses common errors.

Manage GitLab issues, merge requests, CI/CD pipelines, and repositories from the terminal using the glab CLI, including support for self-hosted instances and automation scripts.

Detonate granular AWS, Azure, GCP, and Kubernetes attack techniques to validate detections with Stratus Red Team.

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

Manages a local GitHub knowledge base and searches GitHub via the gh CLI, covering repos, issues, and pull requests.

Deploy, monitor, and manage GCP services with battle-tested patterns.

Reserved and on-demand GPU cloud instances for ML training and inference. Use when you need dedicated GPU instances with simple SSH access, persistent filesystems, or high-performance multi-node clusters for large-scale training.

Deploys and manages Google Cloud Platform backends for Flutter apps, covering GKE clusters, Cloud Run services, Cloud SQL databases, Cloud Storage buckets, and Firebase integration.

**Trigger**: Use when working with the Google Cloud CLI (`gcloud`) — authentication, resource management, configuration, and troubleshooting.

Interacts with Google Cloud services using the gcloud CLI safely and efficiently, with command validation, data reduction, safety guardrails, and workflows for discovery and investigation.

Deploys and optimizes AI/ML inference workloads on GKE, using GPUs, TPUs, and model servers.

Plans, executes, and validates Google Kubernetes Engine (GKE) cluster upgrades and maintenance operations for both Standard and Autopilot clusters, producing upgrade plans, checklists, and runbooks with gcloud commands.

Guides a developer through first steps on Google Cloud, including account setup, authentication, project creation, and billing linkage.

Deploys a secure, enterprise-grade Google Cloud landing zone foundation with organization policies, resource hierarchy, billing association, and centralized logging and monitoring.

Configures GKE storage including PVCs, PersistentVolumes, Filestore, and GCS FUSE with best practices for production workloads.

Create, update, and manage GitHub issues using MCP tools and the GitHub CLI.

Provides guidance on enabling and optimizing GKE Cluster Autoscaler, including Node Auto Provisioning, troubleshooting scale-up/down issues, and best practices for capacity management.

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

Manages Google AlloyDB for PostgreSQL clusters and instances via gcloud CLI, covering discovery, instance analysis, query insights, backup configuration, and maintenance windows.

Generates production-grade Kubernetes manifests including Deployments with probes and security contexts, Services, Ingress with TLS, HPA, PDB, NetworkPolicy, ConfigMaps, and Secrets, with optional Helm charts, Kustomize overlays, Istio mesh, and ArgoCD GitOps.

Expert en infrastructure GCP (GKE, Cloud Run, BigQuery, Pub/Sub, Firestore, Cloud Functions)

Manage GitHub repositories end-to-end: clone, create, fork, configure settings, protect branches, set secrets, create releases, and run workflows using gh or git+curl.

Chunked, compressed N-dimensional arrays for cloud storage with Zarr — parallel I/O, S3/GCS integration, and NumPy/Dask/Xarray compatibility. Use when storing or reading large N-D scientific arrays, streaming chunked data to/from cloud object stores, or building large-scale scientific computing pipelines. Part of the AlterLab Academic Skills suite.

Enumerate and audit GCP storage buckets and IAM policies using GCPBucketBrute and gcloud CLI to identify privilege escalation paths and overly permissive access.

Run computational biology tools for protein structure prediction, design, docking, and molecular dynamics on managed cloud GPUs via REST API or MCP server.

Expert Kubernetes architect specializing in cloud-native infrastructure, advanced GitOps workflows (ArgoCD/Flux), and enterprise container orchestration. Masters EKS/AKS/GKE, service mesh (Istio/Linkerd), progressive delivery, multi-tenancy, and platform engineering. Handles security, observability, cost optimization, and developer experience. Use PROACTIVELY for K8s architecture, GitOps implementation, or cloud-native platform design.

Build the proof-of-concept automation that backs a freelance bid — turns a drafted gig's solution shape into a real, validated n8n workflow via the n8n Cloud MCP (SDK flow), publishes it, and writes the live workflow URL back onto the gig's Notion row. The Prove phase of the Inbound Gig Engine.

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.

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.

Land and deploy workflow. Merges the PR, waits for CI and deploy, verifies production health via canary checks. Takes over after /ship creates the PR. Use when: "merge", "land", "deploy", "merge and verify", "land it", "ship it to production". (gstack)

Land and deploy workflow. (gstack)