# Data Analysis Workflow

> This skill MUST be used when planning or executing a full data analysis workflow, including schema inspection, data quality audit, data cleaning, EDA, relationship analysis, feature engineering, modeling, evaluation, and report generation. MUST trigger on requests like 資料分析流程, EDA 到建模, 數據分析規劃, 分析報告產出, or end-to-end analytics workflow.

- Skill: `timlai666/data-analysis-workflow` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add timlai666/data-analysis-workflow`
- Raw SKILL.md: https://api.skillmd.com/api/skills/timlai666/data-analysis-workflow/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Data & Analytics
- Author: TimLai666 (https://skillmd.com/u/timlai666)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/timlai666/data-analysis-workflow

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# Data Analysis Workflow

## Overview

把資料分析任務拆成可重複執行的 12 個步驟，從初始化到最終報告，確保分析完整、可追蹤、可交付。

## Use This Skill

Use when:
- 需要從原始資料一路做完 EDA、建模、評估與洞察。
- 需要把分析任務標準化成固定步驟與輸出物。
- 需要先做分析流程規劃，再開始實作。

Do not use when:
- 只要回答單一指標或一次性查詢，沒有完整分析流程需求。
- 任務僅限資料擷取或單純視覺化，不包含方法論流程。

## Execution Order (Step 0-12)

- Step 0: Initialization
- Step 1: Schema Inspection
- Step 2: Data Quality Audit
- Step 3: Data Cleaning
- Step 4: EDA
- Step 5: Relationship Analysis
- Step 6: Task Identification
- Step 7: Feature Engineering
- Step 8: Modeling
- Step 9: Evaluation
- Step 10: Insight Extraction
- Step 11: Visualization
- Step 12: Report Generation

Note: 詳細規則、判斷條件、範例與輸出格式請讀 [references/data-analysis-flow.md](./references/data-analysis-flow.md)。

## Expected Deliverables

- `schema_report`
- `eda_report`
- `model_performance`
- `analysis_report`

建議同時保留中間產物：
- `clean_dataset`
- `relationship_report`
- `insight_summary`
- `visualization_set`

## Reference Usage Rule

- 需要完整 12 步規則與門檻時：讀 `references/data-analysis-flow.md`。
- 只需要快速執行主幹流程時：優先使用該檔案中的「簡化版流程」。
- 需要完整專案交付（含建模與報告）時：使用該檔案中的「更進階的版本」。