Data Analyst
Analyzes datasets, identifies trends, and generates actionable insights for improved decision-making using advanced statistical methods.
Install on your platform
We auto-selected Claude Code based on this skill’s supported platforms.
Run in terminal (recommended)
claude mcp add ailabs-393-data-analyst npx -- -y @trustedskills/ailabs-393-data-analyst
Or manually add to ~/.claude/settings.json
{
"mcpServers": {
"ailabs-393-data-analyst": {
"command": "npx",
"args": [
"-y",
"@trustedskills/ailabs-393-data-analyst"
]
}
}
}Requires Claude Code (claude CLI). Run claude --version to verify your install.
About This Skill
What it does
This Data Analyst skill enables AI agents to perform comprehensive data analysis on CSV datasets. It automatically identifies and analyzes missing values, applies intelligent imputation techniques based on data characteristics, and generates interactive Plotly Dash dashboards for visualization. The skill supports a complete exploratory data analysis workflow, from initial assessment to actionable insights.
When to use it
- When you need to understand the quality of a CSV dataset, particularly regarding missing values.
- To automatically fill in missing data using appropriate statistical methods (mean, median, mode, KNN, forward fill, constant).
- For creating interactive visualizations and dashboards to explore trends and patterns within your data.
Key capabilities
- Missing Value Analysis: Detects missing values, identifies data types, calculates statistics, and suggests imputation strategies.
- Intelligent Imputation: Applies various imputation methods (mean, median, mode, KNN, forward fill, constant) based on column characteristics.
- Interactive Dashboard Creation: Generates Plotly Dash dashboards with multiple visualization types for data exploration.
Example prompts
- "Analyze the missing values in this CSV file and suggest how to handle them."
- "Impute the missing values in this dataset using the recommended methods from the analysis report."
- "Create a dashboard visualizing the trends in this cleaned dataset."
Tips & gotchas
- The skill requires Python 3.
- Columns with over 70% missing data will be automatically dropped. Critical ID columns cannot have missing values and will cause rows to be dropped.
- Review the output analysis report before imputing, as it provides valuable insights into the recommended imputation strategies.
Tags
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| Socket | Pass |
| Snyk | Pass |
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