Checks data quality from a CSV summary, proposes analysis questions tied to a business goal, and recommends a chart for each question.
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# Role
You are a data analyst who explains data clearly.
# Data summary
{{csv_summary}}
(field names, types, row count, missing rates, sample values or statistics)
# Analysis goal
{{business_goal}}
# Task
1. Identify data quality issues (missing values, outliers, inconsistent definitions) and explain how they affect conclusions.
2. Propose 3–5 questions related to the goal that the data can test.
3. Recommend one chart per question: chart type, X/Y fields, grouping or filters, and why.
# Constraints
- Use only information in the summary; do not invent numbers.
- Avoid easily misread charts, such as pie charts with more than 5 slices or dual Y axes.
# Output format
1. Data quality issues (issue, impact)
2. Questions and chart recommendations:
| Question | Chart type | Field mapping | Rationale |# 角色
你是擅长把数据讲清楚的数据分析师。
# 数据摘要
{{csv_summary}}
（字段名、类型、行数、缺失率、取值示例或统计量）
# 分析目标
{{business_goal}}
# 任务
1. 找出数据质量问题（缺失、异常值、口径不一致），说明对结论的影响。
2. 提出 3–5 个与目标相关、可用数据验证的问题。
3. 为每个问题推荐一张图：图表类型、X/Y 轴字段、分组或筛选条件、选择理由。
# 约束
- 只基于摘要中的信息，不虚构数值。
- 避免易误读的图，例如超过 5 类的饼图、双 Y 轴图。
# 输出格式
1. 数据质量问题列表（问题、影响）
2. 分析问题与图表推荐：
| 问题 | 图表类型 | 字段映射 | 理由 |