A streamlined CLI tool for automated CSV data profiling and statistical field analysis.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install csv-profile-stepper
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 csv-profile-stepper 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 csv-profile-stepper?
csv-profile-stepper is a specialized utility designed for developers and data scientists who need immediate, granular insights into their CSV datasets. As a featured part of the Openclaw Skills ecosystem, it provides a non-interactive, programmatic way to audit data quality by calculating field types, null value ratios, and unique value counts without the overhead of heavy BI software.
Built for efficiency, this skill focuses strictly on the data provided via the command line, ensuring a predictable and secure workflow for data preprocessing. By integrating this into your Openclaw Skills collection, you can automate the initial discovery phase of data engineering, identifying inconsistencies in record counts or data types before they propagate through your pipeline.
csv-profile-stepper Use Cases
- Rapidly assessing data quality of large CSV exports before database ingestion.
- Identifying missing data patterns (null rates) across specific datasets.
- Verifying the cardinality of columns to determine potential primary keys or categorical features.
- Automating data validation steps within a larger AI-driven data pipeline.
How csv-profile-stepper Works
- The user prepares the target CSV file within the local skill environment.
- The main.py script is executed with specific input and output flags via the CLI.
- The tool parses the CSV structure to determine column metadata and statistical distribution.
- A comprehensive profile is generated, detailing data types and unique constraints.
- The results are exported to a structured JSON or TXT file for further analysis or programmatic consumption.
csv-profile-stepper Setup
To deploy this tool from your Openclaw Skills directory, ensure you have a Python environment ready. Follow these steps to initialize:
# Navigate to the skill directory
cd csv-profile-stepper
# Review available parameters
python main.py --help
# Execute a profile on your dataset
python main.py --input <path_to_your_data.csv> --output profile.json
csv-profile-stepper Data Schema & Taxonomy
The skill produces a structured report containing the following metadata for every column identified in the source file:
| Field Name | Description |
|---|---|
| Field Type | Inferred data type (e.g., Integer, String, Float) |
| Null Rate | The percentage of missing values in the column |
| Unique Count | Total number of distinct entries |
| Row Count | Total number of processed records |
Output files are typically saved as profile.json or output.txt depending on your CLI arguments.
csv-profile-stepper Advanced Features
- Headless CLI Execution: Designed for seamless integration into automated shell scripts and CI/CD pipelines.
- Error Recovery: Built-in help and parameter validation to assist users in correcting input errors immediately.
- Deterministic Output: Ensures repeatable results by strictly processing only specified input files with no implicit side effects.
- Flexible Export Formats: Supports both human-readable text and machine-readable JSON outputs for cross-tool compatibility within the Openclaw Skills ecosystem.
SKILL.md
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