An enhanced data analysis kit providing comprehensive Python and R code templates, statistical methods, visualization guides, and automated reporting formats.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install data-analysis-plus
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install data-analysis-plus using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Data Analysis Plus?
Data Analysis Plus is a comprehensive analytical extension tailored for AI coding workflows. It provides production-ready code blocks across major data science frameworks like Python (Pandas, Scipy, Scikit-Learn, Statsmodels) and R (Dplyr, Ggplot2). By introducing high-quality syntax for everything from basic statistical operations to machine learning models, the skill helps developers minimize engineering overhead and maximize mathematical precision during quantitative exploration.
This framework standardizes analytical methodologies across teams by housing structured reference guides directly within the developer environment. Whether assessing complex regressions, managing data quality checks, or establishing dynamic visualizations, Openclaw Skills empower developers to rapidly translate messy, raw data files into decision-ready business intelligence outputs.
Data Analysis Plus Use Cases
- Running exploratory data analysis (EDA) and computing descriptive statistics across large CSV, Excel, or JSON datasets.
- Conducting rigorous scientific hypothesis testing such as Independent T-Tests, Chi-Square Contingency tests, and one-way ANOVA simulations.
- Developing predictive analytics models using linear regression, logistic regression, and standardized K-Means clustering.
- Executing time-series analysis with automated seasonal decomposition configurations.
- Generating standardized executive summaries and highly granular technical markdown reports.
How Data Analysis Plus Works
- Data Source Ingestion: Connect and ingest structured data from varied local files (CSV, Excel, JSON) or direct SQL database connections.
- Quality Validation & Cleaning: Run automated structural inspections to isolate missing data points, remove duplicates, and prune out mathematical outliers using Interquartile Range calculations.
- Methodological Routing: Choose the correct statistical approach based on data criteria by executing structural decision trees for categorical or distribution profiles.
- Model Execution: Implement modular analysis routines across Python or R language engines using standard numerical libraries.
- Visualization Layout: Render optimized vector charts such as correlation heatmaps, linear trendlines, and scatterplots.
- Report Compilation: Automatically assemble research outcomes into structured Executive Summaries or deep-dive technical documentation files.
Data Analysis Plus Setup
To leverage this template configuration within your local workspace environment, install the required core packages via your system terminal:
# Install Python dependency matrix
pip install pandas numpy scipy scikit-learn statsmodels matplotlib seaborn sqlalchemy
# Open your local R console environment to provision components
R -e "install.packages(c('readr', 'readxl', 'jsonlite', 'dplyr', 'cluster', 'ggplot2'))"
Data Analysis Plus Data Schema & Taxonomy
Data Analysis Plus manages workflows by structuring templates around standardized data operations, mapping analytical logic to precise code blocks:
| Process Layer | Python Module | R Library / Object |
|---|---|---|
| Data Ingestion | pandas Core IO engine |
readr, readxl, jsonlite |
| Statistical Testing | scipy.stats Package |
Base stats framework components |
| Machine Learning | sklearn.linear_model, sklearn.cluster |
Base lm, glm, cluster package |
| Time Series Forecasting | statsmodels.tsa.seasonal |
forecast::auto.arima() functional logic |
| Data Visualization | matplotlib.pyplot, seaborn |
ggplot2 grammar engines |
Data Analysis Plus Advanced Features
- Hypothesis Testing Decision Matrix: An integrated conceptual decision workflow that automatically guides users toward valid mathematical testing selections based on structural variables.
- Algorithmic Sample Size Calculation: Built-in programmatic power analysis utilities using
TTestIndPowerto determine operational testing boundaries prior to active data collection. - Dual-Language Interoperability: Identical computational scaffolding maps analytical procedures interchangeably across Python scripts and R execution pipelines.
- Production-Ready Visualization Formats: Pre-configured plot specifications featuring high-resolution standard outputs (
dpi=300), optimized sizing controls, and clean themes.
SKILL.md
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