# data-analyst

This skill checks CSV data for gaps, fills missing values with methods suited to each column, and builds interactive Plotly Dash views for exploratory analysis.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 1200 installs · ★ 458
- Author: ailabs-393
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/ailabs-393/ai-labs-claude-skills/tree/HEAD/dist/skills/data-analyst/SKILL.md
- Updated: 2025-11-11
- HTML: https://affpapa.org/en/nevibecode/skills/ailabs-393-data-analyst

## What it does

The skill includes scripts to inspect missing values, impute data, and create a dashboard. `analyze_missing_values.py` produces a JSON report with column types and suggested strategies. Then `impute_missing_values.py` can apply methods such as median for skewed numeric data, mode for categories, or KNN for correlated numeric columns.

The imputation script drops columns with more than 70% missing values and rows with missing critical ID fields. Those records and columns will not remain in the cleaned dataset. `create_dashboard.py` builds distribution, time-series, and correlation views, then starts a Dash web server. The scripts use `python3`; dashboard creation can also run on a dataset that has already been cleaned.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add ailabs-393/ai-labs-claude-skills --skill data-analyst -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/data-analyst/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/data-analyst/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add ailabs-393/ai-labs-claude-skills --skill data-analyst -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/data-analyst/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add ailabs-393/ai-labs-claude-skills --skill data-analyst -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/data-analyst/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install ailabs-393/ai-labs-claude-skills data-analyst
```

2. **Manually: put the files in place**
- File: `.github/skills/data-analyst/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add ailabs-393/ai-labs-claude-skills --skill data-analyst -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/ailabs-393/ai-labs-claude-skills --path dist/skills/data-analyst
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/data-analyst/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add ailabs-393/ai-labs-claude-skills --skill data-analyst -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/ailabs-393/ai-labs-claude-skills/tree/HEAD/dist/skills/data-analyst/SKILL.md

## When to use it
- inspect missing values and measure their share by column
- choose imputation methods based on column characteristics
- clean a CSV and produce an imputation report
- explore distributions and correlations in an interactive dashboard

## Example prompts
- Check `data.csv` for missing values and save the recommendations to `analysis.json`.
- Fill the gaps in `data.csv` using `analysis.json`, then save the result as `data_imputed.csv`.
- Build a Plotly Dash dashboard from `data_imputed.csv` in `./visualizations` on port `8050`.

## Source text · SKILL.md · MIT

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````text
---
name: data-analyst
description: This skill should be used when analyzing CSV datasets, handling missing values through intelligent imputation, and creating interactive dashboards to visualize data trends. Use this skill for tasks involving data quality assessment, automated missing value detection and filling, statistical analysis, and generating Plotly Dash dashboards for exploratory data analysis.
---

# Data Analyst

## Overview

This skill provides comprehensive capabilities for data analysis workflows on CSV datasets. It automatically analyzes missing value patterns, intelligently imputes missing data using appropriate statistical methods, and creates interactive Plotly Dash dashboards for visualizing trends and patterns. The skill combines automated missing value handling with rich interactive visualizations to support end-to-end exploratory data analysis.

## Core Capabilities

The data-analyst skill provides three main capabilities that can be used independently or as a complete workflow:

### 1. Missing Value Analysis
Automatically detect and analyze missing values in datasets, identifying patterns and suggesting optimal imputation strategies.

### 2. Intelligent Imputation
Apply sophisticated imputation methods tailored to each column's data type and distribution characteristics.

### 3. Interactive Dashboard Creation
Generate comprehensive Plotly Dash dashboards with multiple visualization types for trend analysis and exploration.

## Complete Workflow

When a user requests complete data analysis with missing value handling and visualization, follow this workflow:

### Step 1: Analyze Missing Values

Run the missing value analysis script to understand the data quality:

```bash
python3 scripts/analyze_missing_values.py <input_file.csv> <output_analysis.json>
```

**What this does**:
- Detects missing values in each column
- Identifies data types (numeric, categorical, temporal, etc.)
- Calculates missing value statistics
- Suggests appropriate imputation strategies per column
- Generates detailed JSON report and console output

**Review the output** to understand:
- Which columns have missing data
- The percentage of missing values
- The recommended imputation method for each column
- Why each method was recommended

### Step 2: Impute Missing Values

Apply automatic imputation based on the analysis:

```bash
python3 scripts/impute_missing_values.py <input_file.csv> <analysis.json> <output_imputed.csv>
```

**What this does**:
- Loads the analysis results (or performs analysis if not provided)
- Applies the optimal imputation method to each column:
  - **Mean**: For normally distributed numeric data
  - **Median**: For skewed numeric data
  - **Mode**: For categorical variables
  - **KNN**: For multivariate numeric data with correlations
  - **Forward fill**: For time series data
````
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First 2845 of 10864 bytes. © ailabs-393 · MIT · https://github.com/ailabs-393/ai-labs-claude-skills/tree/HEAD/dist/skills/data-analyst/SKILL.md

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