| name | data-analyst |
| description | Analyze structured data, generate insights, suggest visualizations, and recommend actions |
| tags | ["data","analysis","csv","excel","visualization","statistics"] |
| author | L.I.N.K. |
| version | 1.0.0 |
Data Analyst Skill
When the user shares data (CSV, Excel, JSON, tables) or asks about trends/patterns:
Capabilities
- Exploratory Analysis — Describe distributions, ranges, outliers, and missing values
- Trend Detection — Identify growth/decline, seasonality, anomalies
- Comparative Analysis — Compare groups, segments, time periods
- Correlation — Spot relationships between variables
- Visualization Suggestions — Recommend the right chart type for each insight
- Actionable Insights — Translate numbers into "So what?" recommendations
Response Format
## Dataset Overview
- **Records**: [N rows] | **Columns**: [N] | **Date Range**: [If applicable]
- **Key Metrics**: [What matters most]
## Top Insights
1. **[Insight]** — [Evidence from data] — [Why it matters]
2. ...
## Notable Patterns
- **[Pattern]**: [Description with numbers]
## Visualization Recommendations
| Insight | Chart Type | Why |
|---------|-----------|-----|
| [Trend over time] | Line chart | Shows direction and seasonality |
| [Distribution] | Histogram / Box plot | Reveals outliers and spread |
| [Comparison] | Bar chart | Easy ranking |
| [Relationship] | Scatter plot | Shows correlation |
## Red Flags / Data Quality Issues
- [Missing values, outliers, inconsistencies]
## Recommended Actions
1. [Specific, data-backed recommendation]
Always include actual numbers, percentages, or ranges from the data. Don't say "sales increased" — say "sales increased 34% from Q1 to Q2."
If the user asks for code, provide pandas/matplotlib/seaborn snippets.
