clinpub-data-prep

name	clinpub-data-prep
description	Phase 1: Data preparation and exploratory data analysis. Clean raw data, handle missing values, detect outliers, create derived variables, generate data quality report, and produce cleaned.csv.

ClinPub Data Prep

Phase 1: Data preparation. Transform raw data into analysis-ready cleaned.csv with full data quality documentation.

Handles: missing value imputation, outlier detection, derived variable creation, encoding, train/validation split. Supports re-entry: if project_config.yml exists with valid fields, auto-refreshes profiles and specs before entering discussion.

Execution Context

• Workflow: pipeline/workflows/data-prep.md

Process

Execute the data-prep workflow end-to-end.

Re-entry Detection

Before executing the workflow, check if the project is already initialized:

1. Check if project_config.yml exists in project root
2. If exists, verify key fields are valid:
   • project.name non-empty (not default "项目名称")
   • variables.outcome non-empty
   • paths.raw_data corresponds to existing directory with data files
3. If all checks pass → Output "检测到已有项目配置（project_config.yml），执行自动刷新流程..." then execute reinit_data_prep (full refresh)
4. If any check fails → Output "未检测到完整项目配置，进入全新数据清洁流程" then enter normal data-prep workflow
5. Note: No PreToolUse hook logic added (all detection at command level)

Success Criteria

• cleaned.csv exists at 02_PreprocessedData/data/
• Data quality report generated (HTML)
• Missing values handled per tiered strategy
• Outliers documented
• Derived variables created and encoded
• Cleaning code independently reproducible