Agent skill · data analytics · ailabs-393
data-analyst
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.
Why this skill is useful
Adds 3 executable scripts for missing value analysis, imputation, and interactive dashboard creation that enhance data analysis capabilities.
What it needs
Requires plotly, python3 installed locally. About 6k tokens when loaded. Last updated 2025-11-11. 438 stars on the source repository.
What this skill does
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: 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: 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 …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills ailabs-393/data-analyst--72f290