{
  "id": "lib-skill:data-science-ml--mlflow-experiment-tracker",
  "_kind": "LibrarySkill",
  "_file": "generated-library/skills.yaml",
  "_cluster": "generated-library",
  "attributes": {
    "displayName": "mlflow-experiment-tracker",
    "description": "MLflow integration skill for experiment tracking, model registry, and artifact management. Enables LLMs to log experiments, compare runs, manage model lifecycle, and retrieve artifacts through the MLflow API.",
    "libraryPath": "library/specializations/data-science-ml/skills/mlflow-experiment-tracker/SKILL.md",
    "specialization": "data-science-ml",
    "contentSummary": "# MLflow Experiment Tracker\n\nIntegrate with MLflow for comprehensive ML experiment tracking, model registry operations, and artifact management.\n\n## Overview\n\nThis skill provides capabilities for interacting with MLflow's tracking server and model registry. It enables automated experiment logg"
  },
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      "attributes": {
        "weight": 1
      }
    },
    {
      "from": "lib-skill:data-science-ml--mlflow-experiment-tracker",
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      "attributes": {
        "weight": 0.7
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      "attributes": {
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    },
    {
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      "attributes": {
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        "weight": 0.7
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    },
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      "kind": "lib_implements_workflow",
      "attributes": {
        "weight": 1
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    },
    {
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      "attributes": {
        "weight": 0.7
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      "attributes": {
        "weight": 1
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    }
  ],
  "incomingEdges": []
}