菜单
Train and deploy propensity models using Databricks AutoML. Use when the agent needs campaign-specific propensity scores and no Model Serving endpoint exists yet, or when retraining is needed for a new campaign type.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Best practices for cross-campaign CDP disposition reasoning. Use when evaluating customer engagement decisions involving frequency caps, campaign prioritization, channel selection, and send-time optimization.
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A simple greeting skill that says hello to a user by name. Use when the user asks for a greeting or hello message.
| name | automl-propensity |
| description | Train and deploy propensity models using Databricks AutoML. Use when the agent needs campaign-specific propensity scores and no Model Serving endpoint exists yet, or when retraining is needed for a new campaign type. |
Spawn a Databricks AutoML workflow to train a propensity model for a specific campaign type (e.g., cart_recovery, seasonal_purchase, loyalty_renewal). The trained model is registered in Unity Catalog and deployed to a Model Serving endpoint.
maestro_cdp.models.propensity_{campaign_type}score_propensity tool to calltrain_propensity.pyTrain a propensity model for a given campaign type.
Arguments:
--campaign-type (required): The campaign type to train for (e.g., cart_recovery)--training-table (optional): UC table with training data (default: maestro_cdp.features.customer_events)--target-column (optional): Target column name (default: converted)--profile (optional): Databricks CLI profile (default: 9cefok)Output: JSON with model_name, endpoint_name, experiment_url, accuracy
score_propensity returns cached synthetic scores (0.81 for Cindy). This skill is discoverable but not invoked.score_propensity calls it instead of returning cached data.