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    ],
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    "primaryDoc": "SKILL.md",
    "sections": [
      {
        "title": "Luban CLI Skill",
        "body": "This skill provides a structured framework for developing and using the Luban CLI, a specialized tool for MLOps management."
      },
      {
        "title": "Core Functionality",
        "body": "The Luban CLI focuses on three primary MLOps pillars:\n\nExperiment Environments (env): Management of development workspaces.\nTraining Tasks (job): Orchestration of model training workloads.\nOnline Services (svc): Deployment and scaling of inference services."
      },
      {
        "title": "Development Workflow",
        "body": "When developing or extending the Luban CLI, follow these steps:\n\nInitialize Project: Use the boilerplate in templates/cli_boilerplate.py as a starting point for the CLI structure.\nDefine Commands: Refer to references/mlops_guide.md for the standard command patterns and required attributes for each entity.\nImplement CRUD: Ensure every entity (env, job, svc) supports the full lifecycle:\n\nCreate: Provisioning new resources.\nRead: Listing and describing existing resources.\nUpdate: Modifying configurations or scaling.\nDelete: Cleaning up resources."
      },
      {
        "title": "Managing Environments",
        "body": "luban env list\nluban env create --name research-v1 --image pytorch:2.0"
      },
      {
        "title": "Managing Training Jobs",
        "body": "luban job create --script train.py --gpu 1\nluban job status --id job_001"
      },
      {
        "title": "Managing Online Services",
        "body": "luban svc create --model-path ./models/v1 --replicas 3\nluban svc scale --id my-service --replicas 5"
      },
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        "body": "templates/cli_boilerplate.py: A Python-based CLI structure using argparse.\nreferences/mlops_guide.md: Detailed specifications for MLOps entities and operations."
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    "body": "Luban CLI Skill\n\nThis skill provides a structured framework for developing and using the Luban CLI, a specialized tool for MLOps management.\n\nCore Functionality\n\nThe Luban CLI focuses on three primary MLOps pillars:\n\nExperiment Environments (env): Management of development workspaces.\nTraining Tasks (job): Orchestration of model training workloads.\nOnline Services (svc): Deployment and scaling of inference services.\nDevelopment Workflow\n\nWhen developing or extending the Luban CLI, follow these steps:\n\nInitialize Project: Use the boilerplate in templates/cli_boilerplate.py as a starting point for the CLI structure.\nDefine Commands: Refer to references/mlops_guide.md for the standard command patterns and required attributes for each entity.\nImplement CRUD: Ensure every entity (env, job, svc) supports the full lifecycle:\nCreate: Provisioning new resources.\nRead: Listing and describing existing resources.\nUpdate: Modifying configurations or scaling.\nDelete: Cleaning up resources.\nUsage Patterns\nManaging Environments\nluban env list\nluban env create --name research-v1 --image pytorch:2.0\n\nManaging Training Jobs\nluban job create --script train.py --gpu 1\nluban job status --id job_001\n\nManaging Online Services\nluban svc create --model-path ./models/v1 --replicas 3\nluban svc scale --id my-service --replicas 5\n\nResources\ntemplates/cli_boilerplate.py: A Python-based CLI structure using argparse.\nreferences/mlops_guide.md: Detailed specifications for MLOps entities and operations."
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    "provenanceUrl": "https://clawhub.ai/guunergooner/luban-cli",
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    "owner": "guunergooner",
    "version": "1.0.0",
    "license": null,
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