An AI-driven assistant designed to automate the cleaning, validation, and transformation of SPSS datasets for research and analysis.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install spss-data-cleaning
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 spss-data-cleaning 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 SPSS Data Cleaning Assistant?
The SPSS Data Cleaning Assistant is a specialized tool within the Openclaw Skills ecosystem tailored for researchers and data analysts. It serves as an AI partner that navigates the tedious aspects of data preparation, including missing value imputation, outlier detection, and data type conversion. By integrating Python's robust statistical libraries, it ensures that raw datasets—whether from surveys, experiments, or public opinion polls—are transformed into clean, reliable SPSS (.sav) or CSV formats.
This skill goes beyond simple filtering by providing intelligent suggestions for handling statistical anomalies. It supports various imputation strategies like regression and hot-decking, detects duplicate records based on key fields, and generates comprehensive Markdown reports. This transparency allows users to maintain a full audit trail of every modification made to their research data, ensuring academic and professional integrity.
SPSS Data Cleaning Assistant Use Cases
- Cleaning survey data by identifying inconsistent logic between age and education levels.
- Imputing missing values in experimental datasets using mean, median, or regression strategies.
- Detecting and handling statistical outliers using Z-score or IQR methods to ensure model accuracy.
- Converting raw Excel or CSV questionnaire exports into properly formatted SPSS .sav files with recoded variables.
- Generating detailed data cleaning logs and summary reports for research documentation.
How SPSS Data Cleaning Assistant Works
- Data Ingestion: The user uploads a data file (.sav, .csv, .xlsx, or .tsv) and defines the research context and cleaning goals.
- Initial Diagnosis: The agent analyzes the dataset to generate a summary of sample size, variable types, missing values, and potential outliers.
- Strategy Proposal: Openclaw Skills suggests a cleaning plan with specific handling methods for each identified issue.
- Execution: Upon user confirmation, the agent performs the cleaning operations using Python-based logic and logs every step.
- Validation & Export: The system verifies the cleaned data against logic rules, produces a final Markdown report, and provides the downloadable cleaned file.
SPSS Data Cleaning Assistant Setup
To use this skill within your Openclaw Skills environment, ensure you have Python 3.8+ and the necessary statistical libraries installed:
pip install pandas scipy openpyxl pyreadstat statsmodels
SPSS Data Cleaning Assistant Data Schema & Taxonomy
The skill manages data through a structured pipeline, resulting in the following outputs:
| File Type | Description |
|---|---|
| Cleaned Data | Exported as SPSS .sav or universal .csv formats. |
| Cleaning Report | A Markdown file detailing sample changes, imputation results, and outlier handling. |
| Processing Log | A timestamped audit trail of all automated operations. |
Metadata Taxonomy
- Sample Metrics: N (Original vs. Final count).
- Variable Info: K (Number of variables), data types, and re-encoding maps.
- Validation Rules: Range checks, logic consistency, and uniqueness constraints.
SPSS Data Cleaning Assistant Advanced Features
- Multi-strategy Imputation: Supports cold-deck, hot-deck, and multiple imputation techniques.
- Custom Logic Validation: Users can define specific Python-based scripts for complex cross-variable logic checks.
- Variable Recoding: Automated transformation of continuous variables into categorical bins or reverse-coding for Likert scales.
- High-Fidelity SPSS Support: Preserves metadata and variable labels when processing native .sav files via pyreadstat.
SKILL.md
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