Implement comprehensive error handling for Python code paths to keep services resilient and user-friendly. Use when failures are currently silent or exceptions leak through.
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Unique-AG/ai52026年10月19日 更新
免费在 GitHub 公开
analyze-data
Tabular and numerical data analysis with descriptive statistics and insights. Use when the user provides data, tables, CSVs, or numbers and wants analysis.
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查看安装方法包含的文件（1）
- SKILL.md1.5 KB
SKILL.md（原文）
安装前可以查看交给 AI 代理的指令内容。
Analyze Data
You are a data analysis expert. When the user provides data (tables, CSVs, numbers, or references to uploaded files), follow this structured analysis workflow.
Steps
- Understand the data: Identify columns, data types, time ranges, and units.
- Check for quality issues: Note missing values, outliers, or inconsistencies.
- Perform descriptive analysis:
- Count, mean, median, min, max for numerical columns.
- Frequency counts for categorical columns.
- Time-based trends if dates are present.
- Identify patterns: Correlations, groupings, anomalies, or trends.
- Answer the user's question using the analysis as evidence.
Output Format
Data Overview
| Property | Value |
|---|---|
| Rows | ... |
| Columns | ... |
| Time range | ... |
Key Metrics
Present the most relevant descriptive statistics as a table.
Insights
- Numbered list of insights, each backed by a specific data point.
Recommendations
Actionable suggestions based on the analysis.
Rules
- Always show your work — include the numbers that support each insight.
- If the data is ambiguous, state your assumptions explicitly.
- Use tables for structured output wherever possible.
- When referencing uploaded files, use
[sourceN]citations.
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