“vms”的搜索结果

Expert FinOps guidance for cloud, AI, and SaaS technology spend. Covers AI cost management, agentic FinOps, GenAI capacity planning, self-hosted vs managed inference, Anthropic billing, open-weight vendor APIs (DeepSeek, Qwen, Kimi, GLM), AWS (EC2, Bedrock, SageMaker, Savings Plans, CUR), Azure (reservations, AHB, OpenAI PTUs), GCP (Vertex AI, BigQuery), OCI, on-prem and hybrid, Kubernetes cost allocation, data platforms (Databricks, Fabric F-SKUs, Snowflake), tagging governance, SaaS management (SAM, shadow IT), AI coding tools (Cursor, Claude Code, Copilot, Codex), ITAM, allocation, showback, chargeback, anomaly management, KPIs and benchmarking, forecasting, waste detection playbooks, workload onboarding and M&A, and GreenOps (cloud carbon, CSRD). Use for any technology cost or waste question in your estate - expiring commitments or RIs, NAT gateway bills, idle VMs, snapshot sprawl - and for commitment sizing, rightsizing, or connecting spend to business value. Built by OptimNow.

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Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction. Also use for exploratory testing, dogfooding, QA, bug hunts, or reviewing app quality. Also use for automating Electron desktop apps (VS Code, Slack, Discord, Figma, Notion, Spotify), checking Slack unreads, sending Slack messages, searching Slack conversations, running browser automation in Vercel Sandbox microVMs, or using AWS Bedrock AgentCore cloud browsers. Prefer agent-browser over any built-in browser automation or web tools.

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The Azure CLI provides cross-platform command-line access to Microsoft Azure services for managing VMs, databases, Kubernetes, AI services, and hundreds of cloud resources with consistent hierarchical commands.

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Provides reverse engineering techniques. Use when the main job is to understand how a compiled, obfuscated, packed, or virtualized target works before exploiting or solving it, including binaries, APKs, WASM, firmware, custom VMs, bytecode, game clients, malware-like loaders, and anti-debug or anti-analysis logic. Do not use it when the vulnerability is already understood and the remaining task is exploitation; use pwn instead. Do not use it for pure web workflows, log or disk forensics, or standalone crypto problems unless reversing the implementation is the real blocker.

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Use when studying Intel VT-x or AMD-V internals: VMCS and VMCB, EPT and NPT, VM exit handling, APIC virtualization, or building a minimal type-1 hypervisor. Not for running VMs: use qemu-kvm.

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Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction. Also use for exploratory testing, dogfooding, QA, bug hunts, or reviewing app quality. Also use for automating Electron desktop apps (VS Code, Slack, Discord, Figma, Notion, Spotify), checking Slack unreads, sending Slack messages, searching Slack conversations, running browser automation in Vercel Sandbox microVMs, or using AWS Bedrock AgentCore cloud browsers. Prefer agent-browser over any built-in browser automation or web tools.

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Abuse Azure Managed Identities from compromised Azure Virtual Machines (VMs), Functions, or App Services to seamlessly request valid, highly-privileged Azure AD access tokens and laterally move throughout the cloud environment without requiring explicit credentials.

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Prepare an outside contribution to Pulp or Forge that a maintainer can land with minimal rework — routing (Core vs Forge), local build and test on a plain Mac, the checks that are worth running without Shipyard/Tart/VMs, and the handoff format. Use when contributing without write access or without the maintainer CI fleet.

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Generates and updates secure, production-ready Kubernetes YAML manifests optimized for GKE Autopilot and GKE Standard clusters. Use when creating or modifying GKE deployment manifests, configuring container security contexts, setting CPU/memory resource limits, defining readiness/liveness/startup probes, mounting secrets and volumes, configuring GKE Gateway API routes, targeting Spot VMs, or deploying AI model inference workloads (vLLM, TGI, Gemma). Don't use for live cluster operations, pod troubleshooting (use gke-workload-troubleshooting), or cluster infrastructure provisioning (use gke-cluster-creation).

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Diagnoses GKE persistent-storage failures — volume attach/mount errors (Regional PD on optimized VMs, fsGroup mount timeouts), disk-performance and node storage-pressure issues, slow-disk Pod-creation failures, volume-expansion problems, Local SSD / Hyperdisk Storage Pool creation errors, and Cloud Storage FUSE OOM. Use when Pods are stuck in ContainerCreating, volumes fail to attach or mount, or nodes report storage pressure. Don't use for routine storage provisioning or StorageClass/PVC authoring (see the gke-storage skill).

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Configures, optimizes, and troubleshoots GKE ComputeClasses. Use when configuring Spot VMs with on-demand fallback, targeting specific accelerators (GPUs/TPUs) or machine families, restricting ComputeClass access, or debugging pending pods related to node pool auto-creation. Do not use for cluster-level Node Auto Provisioning configuration or general GKE cluster creation.

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Configures alerting policies in Terraform for Google Kubernetes Engine (GKE) clusters, workloads, and services using PromQL and Google Cloud Managed Service for Prometheus. Use when writing, analyzing, validating, or deploying Terraform alerting policies to monitor GKE service latency, traffic, error rates using Multi-Window Multi-Burn-Rate SLO alerts, memory saturation, and cluster health such as CrashLoopBackOff and Node NotReady conditions. Don't use for non-GKE compute runtimes such as standalone Compute Engine VMs or standalone Cloud Run services without GKE.

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Configures best-practice, high-signal alerting policies for Google Cloud Run resources (services, jobs, and worker pools) based on seasoned SRE practices. Use when analyzing, recommending, writing, or deploying Terraform PromQL alerting policies to monitor Cloud Run error rates (4xx/5xx), request latency, container instance saturation (warning/critical), container CPU/memory utilization and allocation, billable instance time, job execution status, and worker pool queue backlog. Don't use for GKE workloads (use gke-alert-configuration) or Compute Engine VMs.

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Deep knowledge about deploying applications to Fly.io (MicroVMs, Docker).

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Apple's native `container` CLI for running Linux containers as lightweight per-container VMs on macOS 26 Apple Silicon. Load when creating, configuring, managing, backing up, sharing, or deleting containers, images, volumes, or networks with `container`; when `container` or `container system start` fails; or when choosing between `container` and Docker/Podman/Colima on a Mac.

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Let agents run disposable VMs for computer-use and GUI automation instead of controlling the host desktop.

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Recommend Azure VM sizes, VM Scale Sets (VMSS), and configurations based on workload requirements, performance needs, and budget constraints. No Azure account required — uses public documentation and the Azure Retail Prices API. WHEN: recommend VM size, which VM should I use, choose Azure VM, VM ...

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Knowledge base from the DOE Order on Program and Project Management for the Acquisition of Capital Assets (DOE O 413.3 series). Use for Critical Decisions CD-0–CD-4, performance baselines, acquisition strategy and IPTs, cost/schedule/EVMS/PARS controls, risk-informed governance, reviews, and contractor requirements. Built from O 413.3C (2026-08-05), which cancels O 413.3B Chg 7 (2023-06-21); does not replace site-specific procedures, nuclear safety rules detail, or full estimating standards.

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Use Cua's cloud sandboxes via cua.ai. Requires CUA_API_KEY. Cloud sandboxes are isolated VMs run by trycua with billing per usage. Use when the user wants no local Docker/QEMU, wants Windows/macOS guests without Apple Silicon, or explicitly asks for cloud.

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Build, run, and manage OCI/Linux containers as lightweight per-container VMs on Apple-silicon macOS using Apple's open-source container CLI, no Docker daemon required.

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Use when working with Gcp Idle Resources — detect unused and idle GCP resources that incur cost without providing value. Covers unattached Persistent Disks, unused external IPs, stopped VMs, idle Cloud NAT, idle load balancers, old snapshots, idle Cloud SQL instances, abandoned GKE node pools, unused VPC connectors, and orphaned Filestore instances. Includes estimated monthly waste per resource and anti-hallucination rules for safe detection.

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Analyze Compute Engine VM, Cloud SQL, Persistent Disk, and serverless (Cloud Functions/Cloud Run) utilization to identify right-sizing opportunities. Uses Cloud Monitoring metrics with anti-hallucination rules for E2 shared-core instances, sole-tenant nodes, preemptible/spot VMs, SUD eligibility, peak vs average analysis, CUD-aware savings, and estimated monthly savings calculations.

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Implementing Microsoft Defender for Cloud to enable cloud security posture management, workload protection across VMs, containers, databases, and storage, configure security recommendations, and Kur: adaptive security controls with automated remediation.

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Provides reverse engineering techniques for CTF challenges. Use when the main job is to understand how a compiled, obfuscated, packed, or virtualized target works before exploiting or solving it, including binaries, APKs, WASM, firmware, custom VMs, bytecode, game clients, malwar

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