[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6MB5X8Q72JF3PFJSQMV1SY8":3,"skill-same:01M6MB5X8Q72JF3PFJSQMV1SY8":88},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":50,"commitSha":51,"skillDirectory":52,"forksCount":53,"repositoryHtmlUrl":54,"repositoryHomepageUrl":55,"ownerHtmlUrl":56,"ownerBlogUrl":57,"ownerDescription":58,"updatedAt":59,"install":60,"resources":73},"01M6MB5X8Q72JF3PFJSQMV1SY8","skypilot-multi-cloud-orchestration",null,"Multi-cloud orchestration for ML workloads with automatic cost optimization. Use when you need to run training or batch jobs across multiple clouds, leverage spot instances with auto-recovery, or optimize GPU costs across providers.","hashgraph-online\u002Fawesome-codex-plugins","hashgraph-online","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F160371962?v=4","01M6CNB6NRJVNNGN14TR01Q6D3","HOL (Hashgraph Online)","01M6JT1HSKKG9V68KQAQGBA7XP",1559,"MIT","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fskypilot-multi-cloud-orchestration\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"multi","cloud","multi cloud","skypilot multi cloud orchestration","orchestration","SkyPilot","cost","automatic","for","you","leverage","instances","auto","use","multi-cloud","batch","run","training","infrastructure","cost optimization","2026-11-10 10:12:30",false,"---\nname: skypilot-multi-cloud-orchestration\ndescription: Multi-cloud orchestration for ML workloads with automatic cost optimization. Use when you need to run training or batch jobs across multiple clouds, leverage spot instances with auto-recovery, or optimize GPU costs across providers.\nlicense: MIT\nmetadata:\n  version: 1.0.0\n  category: ml-inference-and-ops\n  maintainer: Kalaris Labs\n  tags: Infrastructure, Multi-Cloud, Orchestration, GPU, Cost Optimization, SkyPilot\n  dependencies: skypilot>=0.7.0\n---\n\n# SkyPilot Multi-Cloud Orchestration\n\nComprehensive guide to running ML workloads across clouds with automatic cost optimization using SkyPilot.\n\n## When to use SkyPilot\n\n**Use SkyPilot when:**\n- Running ML workloads across multiple clouds (AWS, GCP, Azure, etc.)\n- Need cost optimization with automatic cloud\u002Fregion selection\n- Running long jobs on spot instances with auto-recovery\n- Managing distributed multi-node training\n- Want unified interface for 20+ cloud providers\n- Need to avoid vendor lock-in\n\n**Key features:**\n- **Multi-cloud**: AWS, GCP, Azure, Kubernetes, Lambda, RunPod, 20+ providers\n- **Cost optimization**: Automatic cheapest cloud\u002Fregion selection\n- **Spot instances**: 3-6x cost savings with automatic recovery\n- **Distributed training**: Multi-node jobs with gang scheduling\n- **Managed jobs**: Auto-recovery, checkpointing, fault tolerance\n- **Sky Serve**: Model serving with autoscaling\n\n**Use alternatives instead:**\n- **Modal**: For simpler serverless GPU with Python-native API\n- **RunPod**: For single-cloud persistent pods\n- **Kubernetes**: For existing K8s infrastructure\n- **Ray**: For pure Ray-based orchestration\n\n## Quick start\n\n### Installation\n\n```bash\npip install \"skypilot[aws,gcp,azure,kubernetes]\"\n\n# Verify cloud credentials\nsky check\n```\n\n### Hello World\n\nCreate `hello.yaml`:\n```yaml\nresources:\n  accelerators: T4:1\n\nrun: |\n  nvidia-smi\n  echo \"Hello from SkyPilot!\"\n```\n\nLaunch:\n```bash\nsky launch -c hello hello.yaml\n\n# SSH to cluster\nssh hello\n\n# Terminate\nsky down hello\n```\n\n## Core concepts\n\n### Task YAML structure\n\n```yaml\n# Task name (optional)\nname: my-task\n\n# Resource requirements\nresources:\n  cloud: aws              # Optional: auto-select if omitted\n  region: us-west-2       # Optional: auto-select if omitted\n  accelerators: A100:4    # GPU type and count\n  cpus: 8+                # Minimum CPUs\n  memory: 32+             # Minimum memory (GB)\n  use_spot: true          # Use spot instances\n  disk_size: 256          # Disk size (GB)\n\n# Number of nodes for distributed training\nnum_nodes: 2\n\n# Working directory (synced to ~\u002Fsky_workdir)\nworkdir: .\n\n# Setup commands (run once)\nsetup: |\n  pip install -r requirements.txt\n\n# Run commands\nrun: |\n  python train.py\n```\n\n### Key commands\n\n| Command | Purpose |\n|---------|---------|\n| `sky launch` | Launch cluster and run task |\n| `sky exec` | Run task on existing cluster |\n| `sky status` | Show cluster status |\n| `sky stop` | Stop cluster (preserve state) |\n| `sky down` | Terminate cluster |\n| `sky logs` | View task logs |\n| `sky queue` | Show job queue |\n| `sky jobs launch` | Launch managed job |\n| `sky serve up` | Deploy serving endpoint |\n\n## GPU configuration\n\n### Available accelerators\n\n```yaml\n# NVIDIA GPUs\naccelerators: T4:1\naccelerators: L4:1\naccelerators: A10G:1\naccelerators: L40S:1\naccelerators: A100:4\naccelerators: A100-80GB:8\naccelerators: H100:8\n\n# Cloud-specific\naccelerators: V100:4         # AWS\u002FGCP\naccelerators: TPU-v4-8       # GCP TPUs\n```\n\n### GPU fallbacks\n\n```yaml\nresources:\n  accelerators:\n    H100: 8\n    A100-80GB: 8\n    A100: 8\n  any_of:\n    - cloud: gcp\n    - cloud: aws\n    - cloud: azure\n```\n\n### Spot instances\n\n```yaml\nresources:\n  accelerators: A100:8\n  use_spot: true\n  spot_recovery: FAILOVER  # Auto-recover on preemption\n```\n\n## Cluster management\n\n### Launch and execute\n\n```bash\n# Launch new cluster\nsky launch -c mycluster task.yaml\n\n# Run on existing cluster (skip setup)\nsky exec mycluster another_task.yaml\n\n# Interactive SSH\nssh mycluster\n\n# Stream logs\nsky logs mycluster\n```\n\n### Autostop\n\n```yaml\nresources:\n  accelerators: A100:4\n  autostop:\n    idle_minutes: 30\n    down: true  # Terminate instead of stop\n```\n\n```bash\n# Set autostop via CLI\nsky autostop mycluster -i 30 --down\n```\n\n### Cluster status\n\n```bash\n# All clusters\nsky status\n\n# Detailed view\nsky status -a\n```\n\n## Distributed training\n\n### Multi-node setup\n\n```yaml\nresources:\n  accelerators: A100:8\n\nnum_nodes: 4  # 4 nodes × 8 GPUs = 32 GPUs total\n\nsetup: |\n  pip install torch torchvision\n\nrun: |\n  torchrun \\\n    --nnodes=$SKYPILOT_NUM_NODES \\\n    --nproc_per_node=$SKYPILOT_NUM_GPUS_PER_NODE \\\n    --node_rank=$SKYPILOT_NODE_RANK \\\n    --master_addr=$(echo \"$SKYPILOT_NODE_IPS\" | head -n1) \\\n    --master_port=12355 \\\n    train.py\n```\n\n### Environment variables\n\n| Variable | Description |\n|----------|-------------|\n| `SKYPILOT_NODE_RANK` | Node index (0 to num_nodes-1) |\n| `SKYPILOT_NODE_IPS` | Newline-separated IP addresses |\n| `SKYPILOT_NUM_NODES` | Total number of nodes |\n| `SKYPILOT_NUM_GPUS_PER_NODE` | GPUs per node |\n\n### Head-node-only execution\n\n```bash\nrun: |\n  if [ \"${SKYPILOT_NODE_RANK}\" == \"0\" ]; then\n    python orchestrate.py\n  fi\n```\n\n## Managed jobs\n\n### Spot recovery\n\n```bash\n# Launch managed job with spot recovery\nsky jobs launch -n my-job train.yaml\n```\n\n### Checkpointing\n\n```yaml\nname: training-job\n\nfile_mounts:\n  \u002Fcheckpoints:\n    name: my-checkpoints\n    store: s3\n    mode: MOUNT\n\nresources:\n  accelerators: A100:8\n  use_spot: true\n\nrun: |\n  python train.py \\\n    --checkpoint-dir \u002Fcheckpoints \\\n    --resume-from-latest\n```\n\n### Job management\n\n```bash\n# List jobs\nsky jobs queue\n\n# View logs\nsky jobs logs my-job\n\n# Cancel job\nsky jobs cancel my-job\n```\n\n## File mounts and storage\n\n### Local file sync\n\n```yaml\nworkdir: .\u002Fmy-project  # Synced to ~\u002Fsky_workdir\n\nfile_mounts:\n  \u002Fdata\u002Fconfig.yaml: .\u002Fconfig.yaml\n  ~\u002F.vimrc: ~\u002F.vimrc\n```\n\n### Cloud storage\n\n```yaml\nfile_mounts:\n  # Mount S3 bucket\n  \u002Fdatasets:\n    source: s3:\u002F\u002Fmy-bucket\u002Fdatasets\n    mode: MOUNT  # Stream from S3\n\n  # Copy GCS bucket\n  \u002Fmodels:\n    source: gs:\u002F\u002Fmy-bucket\u002Fmodels\n    mode: COPY  # Pre-fetch to disk\n\n  # Cached mount (fast writes)\n  \u002Foutputs:\n    name: my-outputs\n    store: s3\n    mode: MOUNT_CACHED\n```\n\n### Storage modes\n\n| Mode | Description | Best For |\n|------|-------------|----------|\n| `MOUNT` | Stream from cloud | Large datasets, read-heavy |\n| `COPY` | Pre-fetch to disk | Small files, random access |\n| `MOUNT_CACHED` | Cache with async upload | Checkpoints, outputs |\n\n## Sky Serve (Model Serving)\n\nDetails, code examples and parameter tables: [references\u002Fsky-serve-model-serving.md](references\u002Fsky-serve-model-serving.md). Read it when this step applies.\n\n## Cost optimization\n\n### Automatic cloud selection\n\n```yaml\n# SkyPilot finds cheapest option\nresources:\n  accelerators: A100:8\n  # No cloud specified - auto-select cheapest\n```\n\n```bash\n# Show optimizer decision\nsky launch task.yaml --dryrun\n```\n\n### Cloud preferences\n\n```yaml\nresources:\n  accelerators: A100:8\n  any_of:\n    - cloud: gcp\n      region: us-central1\n    - cloud: aws\n      region: us-east-1\n    - cloud: azure\n```\n\n### Environment variables\n\n```yaml\nenvs:\n  HF_TOKEN: $HF_TOKEN  # Inherited from local env\n  WANDB_API_KEY: $WANDB_API_KEY\n\n# Or use secrets\nsecrets:\n  - HF_TOKEN\n  - WANDB_API_KEY\n```\n\n## Common workflows\n\n### Workflow 1: Fine-tuning with checkpoints\n\n```yaml\nname: llm-finetune\n\nfile_mounts:\n  \u002Fcheckpoints:\n    name: finetune-checkpoints\n    store: s3\n    mode: MOUNT_CACHED\n\nresources:\n  accelerators: A100:8\n  use_spot: true\n\nsetup: |\n  pip install transformers accelerate\n\nrun: |\n  python train.py \\\n    --checkpoint-dir \u002Fcheckpoints \\\n    --resume\n```\n\n### Workflow 2: Hyperparameter sweep\n\n```yaml\nname: hp-sweep-${RUN_ID}\n\nenvs:\n  RUN_ID: 0\n  LEARNING_RATE: 1e-4\n  BATCH_SIZE: 32\n\nresources:\n  accelerators: A100:1\n  use_spot: true\n\nrun: |\n  python train.py \\\n    --lr $LEARNING_RATE \\\n    --batch-size $BATCH_SIZE \\\n    --run-id $RUN_ID\n```\n\n```bash\n# Launch multiple jobs\nfor i in {1..10}; do\n  sky jobs launch sweep.yaml \\\n    --env RUN_ID=$i \\\n    --env LEARNING_RATE=$(python -c \"import random; print(10**random.uniform(-5,-3))\")\ndone\n```\n\n## Debugging\n\n```bash\n# SSH to cluster\nssh mycluster\n\n# View logs\nsky logs mycluster\n\n# Check job queue\nsky queue mycluster\n\n# View managed job logs\nsky jobs logs my-job\n```\n\n## Common issues\n\n| Issue | Solution |\n|-------|----------|\n| Quota exceeded | Request quota increase, try different region |\n| Spot preemption | Use `sky jobs launch` for auto-recovery |\n| Slow file sync | Use `MOUNT_CACHED` mode for outputs |\n| GPU not available | Use `any_of` for fallback clouds |\n\n## References\n\n- **[Advanced Usage](references\u002Fadvanced-usage.md)** - Multi-cloud, optimization, production patterns\n- **[Troubleshooting](references\u002Ftroubleshooting.md)** - Common issues and solutions\n\n## Resources\n\n- **Documentation**: https:\u002F\u002Fdocs.skypilot.co\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Fskypilot-org\u002Fskypilot\n- **Slack**: https:\u002F\u002Fslack.skypilot.co\n- **Examples**: https:\u002F\u002Fgithub.com\u002Fskypilot-org\u002Fskypilot\u002Ftree\u002Fmaster\u002Fexamples\n\n## Agent operating procedure\n\n1. **Check the environment.** Confirm hardware, framework and server versions, model format, and expected load.\n2. **Pin down the inputs.** Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.\n3. **Run a small version first.** Serve or log a single request or run end to end before scaling.\n4. **Execute the full task** using the instructions and references above.\n5. **Validate the result.** Measure latency, throughput and output correctness against a reference; check resource usage and costs.\n6. **Report.** State what was run (versions, commands, parameters), what was checked, and what is still uncertain.\n\n| If this happens | Do this |\n|---|---|\n| The server fails to start or OOMs | Check model size versus memory, quantization and parallelism settings. |\n| A function, flag or endpoint in these instructions is missing in the installed version | Check the installed version's own documentation (`help()`, `--help`, official docs), adapt, and tell the user. Never invent an API. |\n| A required input, identifier or parameter is ambiguous | Ask the user, or state the assumption explicitly before running. |\n\n**Integrity rules**\n\n- Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.\n- Do not expose services or credentials publicly; confirm cloud costs before provisioning.\n- Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.\n- Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.\n\n## Related skills\n\n- `modal-serverless-gpu`: Serverless GPU cloud platform for running ML workloads.\n- `lambda-labs-gpu-cloud`: Reserved and on-demand GPU cloud instances for ML training and inference.\n- `ray-train`: Distributed training orchestration across clusters.\n","# SkyPilot Multi-Cloud Orchestration\n\nComprehensive guide to running ML workloads across clouds with automatic cost optimization using SkyPilot.\n\n## When to use SkyPilot\n\n**Use SkyPilot when:**\n- Running ML workloads across multiple clouds (AWS, GCP, Azure, etc.)\n- Need cost optimization with automatic cloud\u002Fregion selection\n- Running long jobs on spot instances with auto-recovery\n- Managing distributed multi-node training\n- Want unified interface for 20+ cloud providers\n- Need to avoid vendor lock-in\n\n**Key features:**\n- **Multi-cloud**: AWS, GCP, Azure, Kubernetes, Lambda, RunPod, 20+ providers\n- **Cost optimization**: Automatic cheapest cloud\u002Fregion selection\n- **Spot instances**: 3-6x cost savings with automatic recovery\n- **Distributed training**: Multi-node jobs with gang scheduling\n- **Managed jobs**: Auto-recovery, checkpointing, fault tolerance\n- **Sky Serve**: Model serving with autoscaling\n\n**Use alternatives instead:**\n- **Modal**: For simpler serverless GPU with Python-native API\n- **RunPod**: For single-cloud persistent pods\n- **Kubernetes**: For existing K8s infrastructure\n- **Ray**: For pure Ray-based orchestration\n\n## Quick start\n\n### Installation\n\n```bash\npip install \"skypilot[aws,gcp,azure,kubernetes]\"\n\n# Verify cloud credentials\nsky check\n```\n\n### Hello World\n\nCreate `hello.yaml`:\n```yaml\nresources:\n  accelerators: T4:1\n\nrun: |\n  nvidia-smi\n  echo \"Hello from SkyPilot!\"\n```\n\nLaunch:\n```bash\nsky launch -c hello hello.yaml\n\n# SSH to cluster\nssh hello\n\n# Terminate\nsky down hello\n```\n\n## Core concepts\n\n### Task YAML structure\n\n```yaml\n# Task name (optional)\nname: my-task\n\n# Resource requirements\nresources:\n  cloud: aws              # Optional: auto-select if omitted\n  region: us-west-2       # Optional: auto-select if omitted\n  accelerators: A100:4    # GPU type and count\n  cpus: 8+                # Minimum CPUs\n  memory: 32+             # Minimum memory (GB)\n  use_spot: true          # Use spot instances\n  disk_size: 256          # Disk size (GB)\n\n# Number of nodes for distributed training\nnum_nodes: 2\n\n# Working directory (synced to ~\u002Fsky_workdir)\nworkdir: .\n\n# Setup commands (run once)\nsetup: |\n  pip install -r requirements.txt\n\n# Run commands\nrun: |\n  python train.py\n```\n\n### Key commands\n\n| Command | Purpose |\n|---------|---------|\n| `sky launch` | Launch cluster and run task |\n| `sky exec` | Run task on existing cluster |\n| `sky status` | Show cluster status |\n| `sky stop` | Stop cluster (preserve state) |\n| `sky down` | Terminate cluster |\n| `sky logs` | View task logs |\n| `sky queue` | Show job queue |\n| `sky jobs launch` | Launch managed job |\n| `sky serve up` | Deploy serving endpoint |\n\n## GPU configuration\n\n### Available accelerators\n\n```yaml\n# NVIDIA GPUs\naccelerators: T4:1\naccelerators: L4:1\naccelerators: A10G:1\naccelerators: L40S:1\naccelerators: A100:4\naccelerators: A100-80GB:8\naccelerators: H100:8\n\n# Cloud-specific\naccelerators: V100:4         # AWS\u002FGCP\naccelerators: TPU-v4-8       # GCP TPUs\n```\n\n### GPU fallbacks\n\n```yaml\nresources:\n  accelerators:\n    H100: 8\n    A100-80GB: 8\n    A100: 8\n  any_of:\n    - cloud: gcp\n    - cloud: aws\n    - cloud: azure\n```\n\n### Spot instances\n\n```yaml\nresources:\n  accelerators: A100:8\n  use_spot: true\n  spot_recovery: FAILOVER  # Auto-recover on preemption\n```\n\n## Cluster management\n\n### Launch and execute\n\n```bash\n# Launch new cluster\nsky launch -c mycluster task.yaml\n\n# Run on existing cluster (skip setup)\nsky exec mycluster another_task.yaml\n\n# Interactive SSH\nssh mycluster\n\n# Stream logs\nsky logs mycluster\n```\n\n### Autostop\n\n```yaml\nresources:\n  accelerators: A100:4\n  autostop:\n    idle_minutes: 30\n    down: true  # Terminate instead of stop\n```\n\n```bash\n# Set autostop via CLI\nsky autostop mycluster -i 30 --down\n```\n\n### Cluster status\n\n```bash\n# All clusters\nsky status\n\n# Detailed view\nsky status -a\n```\n\n## Distributed training\n\n### Multi-node setup\n\n```yaml\nresources:\n  accelerators: A100:8\n\nnum_nodes: 4  # 4 nodes × 8 GPUs = 32 GPUs total\n\nsetup: |\n  pip install torch torchvision\n\nrun: |\n  torchrun \\\n    --nnodes=$SKYPILOT_NUM_NODES \\\n    --nproc_per_node=$SKYPILOT_NUM_GPUS_PER_NODE \\\n    --node_rank=$SKYPILOT_NODE_RANK \\\n    --master_addr=$(echo \"$SKYPILOT_NODE_IPS\" | head -n1) \\\n    --master_port=12355 \\\n    train.py\n```\n\n### Environment variables\n\n| Variable | Description |\n|----------|-------------|\n| `SKYPILOT_NODE_RANK` | Node index (0 to num_nodes-1) |\n| `SKYPILOT_NODE_IPS` | Newline-separated IP addresses |\n| `SKYPILOT_NUM_NODES` | Total number of nodes |\n| `SKYPILOT_NUM_GPUS_PER_NODE` | GPUs per node |\n\n### Head-node-only execution\n\n```bash\nrun: |\n  if [ \"${SKYPILOT_NODE_RANK}\" == \"0\" ]; then\n    python orchestrate.py\n  fi\n```\n\n## Managed jobs\n\n### Spot recovery\n\n```bash\n# Launch managed job with spot recovery\nsky jobs launch -n my-job train.yaml\n```\n\n### Checkpointing\n\n```yaml\nname: training-job\n\nfile_mounts:\n  \u002Fcheckpoints:\n    name: my-checkpoints\n    store: s3\n    mode: MOUNT\n\nresources:\n  accelerators: A100:8\n  use_spot: true\n\nrun: |\n  python train.py \\\n    --checkpoint-dir \u002Fcheckpoints \\\n    --resume-from-latest\n```\n\n### Job management\n\n```bash\n# List jobs\nsky jobs queue\n\n# View logs\nsky jobs logs my-job\n\n# Cancel job\nsky jobs cancel my-job\n```\n\n## File mounts and storage\n\n### Local file sync\n\n```yaml\nworkdir: .\u002Fmy-project  # Synced to ~\u002Fsky_workdir\n\nfile_mounts:\n  \u002Fdata\u002Fconfig.yaml: .\u002Fconfig.yaml\n  ~\u002F.vimrc: ~\u002F.vimrc\n```\n\n### Cloud storage\n\n```yaml\nfile_mounts:\n  # Mount S3 bucket\n  \u002Fdatasets:\n    source: s3:\u002F\u002Fmy-bucket\u002Fdatasets\n    mode: MOUNT  # Stream from S3\n\n  # Copy GCS bucket\n  \u002Fmodels:\n    source: gs:\u002F\u002Fmy-bucket\u002Fmodels\n    mode: COPY  # Pre-fetch to disk\n\n  # Cached mount (fast writes)\n  \u002Foutputs:\n    name: my-outputs\n    store: s3\n    mode: MOUNT_CACHED\n```\n\n### Storage modes\n\n| Mode | Description | Best For |\n|------|-------------|----------|\n| `MOUNT` | Stream from cloud | Large datasets, read-heavy |\n| `COPY` | Pre-fetch to disk | Small files, random access |\n| `MOUNT_CACHED` | Cache with async upload | Checkpoints, outputs |\n\n## Sky Serve (Model Serving)\n\nDetails, code examples and parameter tables: [references\u002Fsky-serve-model-serving.md](references\u002Fsky-serve-model-serving.md). Read it when this step applies.\n\n## Cost optimization\n\n### Automatic cloud selection\n\n```yaml\n# SkyPilot finds cheapest option\nresources:\n  accelerators: A100:8\n  # No cloud specified - auto-select cheapest\n```\n\n```bash\n# Show optimizer decision\nsky launch task.yaml --dryrun\n```\n\n### Cloud preferences\n\n```yaml\nresources:\n  accelerators: A100:8\n  any_of:\n    - cloud: gcp\n      region: us-central1\n    - cloud: aws\n      region: us-east-1\n    - cloud: azure\n```\n\n### Environment variables\n\n```yaml\nenvs:\n  HF_TOKEN: $HF_TOKEN  # Inherited from local env\n  WANDB_API_KEY: $WANDB_API_KEY\n\n# Or use secrets\nsecrets:\n  - HF_TOKEN\n  - WANDB_API_KEY\n```\n\n## Common workflows\n\n### Workflow 1: Fine-tuning with checkpoints\n\n```yaml\nname: llm-finetune\n\nfile_mounts:\n  \u002Fcheckpoints:\n    name: finetune-checkpoints\n    store: s3\n    mode: MOUNT_CACHED\n\nresources:\n  accelerators: A100:8\n  use_spot: true\n\nsetup: |\n  pip install transformers accelerate\n\nrun: |\n  python train.py \\\n    --checkpoint-dir \u002Fcheckpoints \\\n    --resume\n```\n\n### Workflow 2: Hyperparameter sweep\n\n```yaml\nname: hp-sweep-${RUN_ID}\n\nenvs:\n  RUN_ID: 0\n  LEARNING_RATE: 1e-4\n  BATCH_SIZE: 32\n\nresources:\n  accelerators: A100:1\n  use_spot: true\n\nrun: |\n  python train.py \\\n    --lr $LEARNING_RATE \\\n    --batch-size $BATCH_SIZE \\\n    --run-id $RUN_ID\n```\n\n```bash\n# Launch multiple jobs\nfor i in {1..10}; do\n  sky jobs launch sweep.yaml \\\n    --env RUN_ID=$i \\\n    --env LEARNING_RATE=$(python -c \"import random; print(10**random.uniform(-5,-3))\")\ndone\n```\n\n## Debugging\n\n```bash\n# SSH to cluster\nssh mycluster\n\n# View logs\nsky logs mycluster\n\n# Check job queue\nsky queue mycluster\n\n# View managed job logs\nsky jobs logs my-job\n```\n\n## Common issues\n\n| Issue | Solution |\n|-------|----------|\n| Quota exceeded | Request quota increase, try different region |\n| Spot preemption | Use `sky jobs launch` for auto-recovery |\n| Slow file sync | Use `MOUNT_CACHED` mode for outputs |\n| GPU not available | Use `any_of` for fallback clouds |\n\n## References\n\n- **[Advanced Usage](references\u002Fadvanced-usage.md)** - Multi-cloud, optimization, production patterns\n- **[Troubleshooting](references\u002Ftroubleshooting.md)** - Common issues and solutions\n\n## Resources\n\n- **Documentation**: https:\u002F\u002Fdocs.skypilot.co\n- **GitHub**: https:\u002F\u002Fgithub.com\u002Fskypilot-org\u002Fskypilot\n- **Slack**: https:\u002F\u002Fslack.skypilot.co\n- **Examples**: https:\u002F\u002Fgithub.com\u002Fskypilot-org\u002Fskypilot\u002Ftree\u002Fmaster\u002Fexamples\n\n## Agent operating procedure\n\n1. **Check the environment.** Confirm hardware, framework and server versions, model format, and expected load.\n2. **Pin down the inputs.** Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.\n3. **Run a small version first.** Serve or log a single request or run end to end before scaling.\n4. **Execute the full task** using the instructions and references above.\n5. **Validate the result.** Measure latency, throughput and output correctness against a reference; check resource usage and costs.\n6. **Report.** State what was run (versions, commands, parameters), what was checked, and what is still uncertain.\n\n| If this happens | Do this |\n|---|---|\n| The server fails to start or OOMs | Check model size versus memory, quantization and parallelism settings. |\n| A function, flag or endpoint in these instructions is missing in the installed version | Check the installed version's own documentation (`help()`, `--help`, official docs), adapt, and tell the user. Never invent an API. |\n| A required input, identifier or parameter is ambiguous | Ask the user, or state the assumption explicitly before running. |\n\n**Integrity rules**\n\n- Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.\n- Do not expose services or credentials publicly; confirm cloud costs before provisioning.\n- Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.\n- Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.\n\n## Related skills\n\n- `modal-serverless-gpu`: Serverless GPU cloud platform for running ML workloads.\n- `lambda-labs-gpu-cloud`: Reserved and on-demand GPU cloud instances for ML training and inference.\n- `ray-train`: Distributed training orchestration across clusters.\n",{"name":5,"license":15,"metadata":44,"description":7},{"tags":45,"version":46,"category":47,"maintainer":48,"dependencies":49},"Infrastructure, Multi-Cloud, Orchestration, GPU, Cost Optimization, SkyPilot","1.0.0","ml-inference-and-ops","Kalaris Labs","skypilot>=0.7.0","main","2b63f856ce19c2a797929cacabd9e0fc936812fa","plugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fskypilot-multi-cloud-orchestration",474,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins","https:\u002F\u002Fhol.org\u002Fplugins\u002Fbest-codex-plugins","https:\u002F\u002Fgithub.com\u002Fhashgraph-online","https:\u002F\u002Fhol.org","Open-source standards and developer tools for interoperable AI agents: discovery, identity, communication, payments, privacy and security.","2026-11-11 14:40:14",{"source":8,"skillName":5,"commands":61,"prompt":71,"zipAvailable":72},[62,65,68],{"tool":63,"command":64},"npx","npx skills add hashgraph-online\u002Fawesome-codex-plugins --skill skypilot-multi-cloud-orchestration",{"tool":66,"command":67},"bunx","bunx skills add hashgraph-online\u002Fawesome-codex-plugins --skill skypilot-multi-cloud-orchestration",{"tool":69,"command":70},"pnpm","pnpm dlx skills add hashgraph-online\u002Fawesome-codex-plugins --skill skypilot-multi-cloud-orchestration","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fskypilot-multi-cloud-orchestration\u002FSKILL.md",true,[74,80,84],{"path":75,"resourceType":76,"resourceTypeDesc":77,"sizeBytes":78,"sourceHtmlUrl":79},"references\u002Fadvanced-usage.md","REFERENCE","参考文档",7469,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fskypilot-multi-cloud-orchestration\u002Freferences\u002Fadvanced-usage.md",{"path":81,"resourceType":76,"resourceTypeDesc":77,"sizeBytes":82,"sourceHtmlUrl":83},"references\u002Fsky-serve-model-serving.md",732,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fskypilot-multi-cloud-orchestration\u002Freferences\u002Fsky-serve-model-serving.md",{"path":85,"resourceType":76,"resourceTypeDesc":77,"sizeBytes":86,"sourceHtmlUrl":87},"references\u002Ftroubleshooting.md",10493,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fskypilot-multi-cloud-orchestration\u002Freferences\u002Ftroubleshooting.md",[89,98,105,112,138,163],{"skillNo":90,"name":91,"nameZh":6,"description":92,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":93,"licenseFileUrl":6,"sourceHtmlUrl":94,"categories":95,"keywords":96,"publishedAt":97},"01M746ZWACVKMZ1BJXJZHYJSSX","migrate-to-e2e","Moves an existing Playwright, Cypress, Selenium, Appium, Detox, Maestro, or TesterArmy suite to e2e one slice at a time, with the old suite running beside the new one until each ported test is proven. Keeps a MIGRATION.md progress file so any later session resumes where the last stopped. Optionally plants a defect to show both suites catch it. Use for \u002Fmigrate-to-e2e, \"migrate our tests to e2e\", \"port these Maestro flows\", \"move our TesterArmy tests\", or when a project has an e2e config and an older suite still running.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fmigrate-to-e2e\u002FSKILL.md",[],[],"2026-11-11 15:11:14",{"skillNo":99,"name":100,"nameZh":6,"description":101,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":93,"licenseFileUrl":6,"sourceHtmlUrl":102,"categories":103,"keywords":104,"publishedAt":97},"01M746ZPTAX1Q0FBKAB1CYND7E","e2e","Writes, runs, and debugs end-to-end tests for web and mobile apps with e2e, the agentic test runner, which mixes agent.act goals with exact locator assertions on a Playwright browser or an iOS or Android device. Also drives a project's running app live over the e2e MCP server to look at a screen, check a UI change, take a screenshot or record a video, and try a locator; and runs bug bashes, parallel e2e explore runs with each bug proven by a repro test. Use when a project depends on e2e or has an e2e.config.ts, when asked for end-to-end, browser, mobile, or agentic UI tests, to check a change in the running app, to QA or bug bash an app or branch, or when an e2e run fails.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fe2e\u002FSKILL.md",[],[],{"skillNo":106,"name":107,"nameZh":6,"description":108,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":93,"licenseFileUrl":6,"sourceHtmlUrl":109,"categories":110,"keywords":111,"publishedAt":97},"01M746ZJN784HVHMDP9MQGCSGG","create-verification-skill","Generate a project-local verify-\u003Capp> skill on top of e2e, so any coding agent can launch the app, check it is worth driving, exercise a feature the way a user does, keep evidence, and bug bash it. Writes the skill, a feature map with one explore charter per feature, and proves it once. Use for \u002Fcreate-verification-skill, \"make a verification skill for this repo\", or when a project has no scripted way to prove UI behavior.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fcreate-verification-skill\u002FSKILL.md",[],[],{"skillNo":113,"name":114,"nameZh":6,"description":115,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":93,"licenseFileUrl":6,"sourceHtmlUrl":116,"categories":117,"keywords":118,"publishedAt":39},"01M716A6ETNB9B1SQ3GXZC75VR","deja-search","deja-vu memory — search the user's past AI coding sessions with the deja CLI. Holds what they already settled and never wrote into the repo (naming rules, banned or already-tried tools, required checks before merge, preferred values, who owns what). Use before your first edit in a task, before you call a change done or ready to merge, before you recommend a tool, library, design or reviewer, before re-debugging an error that may already be solved, when they say 'didn't we fix this before', 'what did we decide about X' or 'I already have that', and before telling them something on this machine does not exist.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Fvshulcz\u002Fdeja-vu\u002Fskills\u002Fdeja-search\u002FSKILL.md",[],[119,120,121,122,123,124,125,126,127,128,129,130,28,131,132,133,134,135,136,137],"search","deja search","never","error","ready","memory","re","decide","fix","design","debugging","when","done","reviewer","io","change","cli","merge","past",{"skillNo":139,"name":140,"nameZh":6,"description":141,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":93,"licenseFileUrl":6,"sourceHtmlUrl":142,"categories":143,"keywords":144,"publishedAt":39},"01M716A3J2W5SENSE9VGMXB375","deja-reflect","End-of-session reflection and memory curation with deja-vu. Review what was decided, what commands broke and what repaired them, and record durable project rules or decisions with deja remember so the next session starts with them.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Fvshulcz\u002Fdeja-vu\u002Fskills\u002Fdeja-reflect\u002FSKILL.md",[],[145,146,147,133,148,149,150,151,152,153,154,155,156,157,124,158,159,160,161,162],"deja reflect","reflect","review","with","record","github","next","https","node","Rules","decisions","brew","remember","project rules","project","session","end","reflection",{"skillNo":164,"name":165,"nameZh":6,"description":166,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":93,"licenseFileUrl":6,"sourceHtmlUrl":167,"categories":168,"keywords":180,"publishedAt":39},"01M715T3RR4BAYSR57HPPTJ0PF","revid","Make, edit, export and post AI videos with Revid (revid.ai) through the Revid MCP server. Use whenever the user wants a short-form or long-form video made (from an idea, a script, a URL, an article, a PDF, a tweet, a product page, their own clips, or a song), wants to find what is going viral on TikTok, YouTube, Instagram or X before making something, wants to edit or re-export an existing Revid project, generate a standalone AI image or clip, make a series episode, or publish and schedule a finished video to connected social accounts. Covers setup (hosted MCP with OAuth, or an API key), the 36 tools, credits, polling and the rules for publishing. Routes to the more specific revid-* skills when one fits.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftmakersas\u002Frevid-skills\u002Fskills\u002Frevid\u002FSKILL.md",[169,174,177],{"categoryNo":170,"categoryName":171,"taxonomyType":172,"taxonomyTypeDesc":173},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",{"categoryNo":175,"categoryName":176,"taxonomyType":172,"taxonomyTypeDesc":173},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":178,"categoryName":179,"taxonomyType":172,"taxonomyTypeDesc":173},"01JBSEEDCAT000000000000010","后端与接口",[165,181,182,183,184,185,186,159,187,125,188,189,190,191,130,192,193,194,195,196],"URL","Video","short-form","standalone","PDF","viral","tiktok","short","social","MCP","song","image","mcp server","schedule","api","publish"]