---
name: Data Analyzer
slug: data-analyzer-3
category: Data
description: Data Analyzer helps analyze structured data, compute descriptive statistics, and summarize patterns or differences. Use it to clean data, compare groups or time periods, and produce analytical reports.
github: "https://github.com/vixues/LeAgent/tree/main/backend/leagent/skills/builtin/data-analyzer"
language: Python
stars: 218
forks: 40
install: "npx degit https://github.com/vixues/LeAgent/tree/main/backend/leagent/skills/builtin/data-analyzer ~/.claude/skills/data-analyzer"
installs_to: ~/.claude/skills/data-analyzer
source_path: backend/leagent/skills/builtin/data-analyzer/SKILL.md
collection_size: 7
category_size: 729
collection_url: "https://dirskills.com/collections/vixues/LeAgent"
added: 2026-09-04T05:25:21.377Z
last_synced: 2026-09-04T05:25:21.377Z
canonical_url: "https://dirskills.com/skills/data-analyzer-3"
---

# Data Analyzer

Data Analyzer helps analyze structured data, compute descriptive statistics, and summarize patterns or differences. Use it to clean data, compare groups or time periods, and produce analytical reports.

**Install:**

```bash
npx degit https://github.com/vixues/LeAgent/tree/main/backend/leagent/skills/builtin/data-analyzer ~/.claude/skills/data-analyzer
```

## README

# Data Analysis

You are assisting with data analysis tasks. Follow these guidelines.

## Analysis Workflow

1. **Understand** the data: identify columns, types, ranges, and any quality issues.
2. **Clean** the data: handle missing values, outliers, and format inconsistencies.
3. **Analyze**: compute relevant statistics (counts, sums, averages, distributions).
4. **Compare**: when multiple datasets or time periods exist, provide comparative analysis.
5. **Summarize**: present findings clearly with key metrics highlighted.

## Statistical Methods

- Use descriptive statistics (mean, median, mode, std dev) as a baseline.
- Identify trends and patterns — year-over-year, month-over-month, category breakdowns.
- Flag outliers and anomalies with context about their potential significance.
- For comparisons, compute both absolute and percentage differences.

## Output Formats

- **Summary**: Concise paragraph with key findings and numbers.
- **Table**: Structured tabular format for detailed breakdowns.
- **Report**: Sectioned report with executive summary, methodology, findings, and recommendations.

## Best Practices

- Always state the sample size and time range of the data being analyzed.
- Round numbers appropriately for readability (2 decimal places for percentages).
- When making comparisons, ensure the baseline and comparison period are clear.
- Distinguish between correlation and causation in findings.
- Provide actionable recommendations when the analysis supports them.