A comprehensive Python-based profiling engine designed to assess the quality, patterns, and distributions of construction industry datasets.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install data-profiler
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-profiler 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 Profiler for Construction?
The Data Profiler for Construction is a specialized diagnostic tool used to understand the characteristics and integrity of industry-specific data. By utilizing Openclaw Skills, this tool helps engineers and analysts identify data types, missing patterns, and anomalies before they impact project outcomes. It is particularly effective at recognizing complex construction metadata, such as CSI codes and project IDs, ensuring that your data pipelines are built on a foundation of clean, validated information.
This skill automates the tedious process of manual data inspection, providing technical teams with a clear picture of their data health. It goes beyond basic statistics to offer industry-contextual insights, making it an essential component for any data-driven construction firm looking to optimize their digital workflows.
Data Profiler for Construction Use Cases
- Pre-ETL analysis to identify source data issues before building integration pipelines.
- Continuous quality monitoring to track data health and integrity over project lifecycles.
- Automated schema validation to detect unexpected changes in external data structures.
- Outlier and anomaly detection to find cost discrepancies or scheduling irregularities.
How Data Profiler for Construction Works
- Import the construction dataset into a structured Pandas DataFrame for processing.
- Run the profiling engine to evaluate column-level statistics, including null counts, uniqueness, and distribution metrics.
- Apply specialized inference logic to detect construction-specific types like cost codes, MasterFormat codes, and project IDs.
- Calculate a comprehensive quality score (0-100) based on missing values and detected anomalies.
- Generate a human-readable Markdown report or a machine-parseable JSON profile for further integration.
- Optionally compare the current profile with historical snapshots to detect data drift or schema changes.
Data Profiler for Construction Setup
To integrate this capability into your workflow using Openclaw Skills, ensure you have Python 3.x and the necessary libraries installed via pip:
pip install pandas numpy
You can then import the profiler into your script and begin analyzing Excel, CSV, or database sources directly.
Data Profiler for Construction Data Schema & Taxonomy
The skill generates structured metadata that organizes data health insights into a clear taxonomy. The following table describes the primary data points captured:
| Attribute | Description |
|---|---|
inferred_type |
Industry-specific type such as csi_code, project_id, or cost_code. |
quality_score |
A 0-100 score indicating the overall health of the dataset. |
null_percentage |
The ratio of missing values per column. |
quality_issues |
A list of specific flags like 'negative costs' or 'outliers detected'. |
recommendations |
Actionable steps to clean or transform the data based on the profile. |
Data Profiler for Construction Advanced Features
- Smart pattern recognition for CSI MasterFormat, WBS, and specialized construction IDs.
- Multi-profile comparison to track how data quality evolves between different project phases.
- Automated detection of cost-specific anomalies, including negative currency values and budget outliers.
- Seamless JSON export support for integrating data quality metrics into broader enterprise dashboards.
- Built-in Markdown report generator for instant technical documentation and stakeholder sharing.
SKILL.md
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METADATA
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