{
  "id": "scientific/pyhealth",
  "version": "1.0.0",
  "name": "pyhealth",
  "description": "Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. This skill should be used when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare datasets (MIMIC-III/IV, eICU, OMOP), or implementing deep learning models for healthcare applications (RETAIN, SafeDrug, Transformer, GNN).",
  "domain": "scientific",
  "source": "external",
  "tools": [],
  "exitStates": [
    "success",
    "failure"
  ],
  "inputSchema": {},
  "outputSchema": {},
  "temperature": 0,
  "tags": [
    "scientific",
    "imported",
    "community"
  ],
  "platforms": {
    "claude": {
      "license": "MIT license",
      "triggers": [
        "help with pyhealth",
        "data analysis"
      ]
    },
    "cursor": {},
    "skeneflow": {}
  }
}
