A specialized Python-based parser designed to convert complex construction industry XML files into structured pandas DataFrames.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install xml-reader
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 xml-reader 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 XML Reader for Construction Data?
This skill provides a robust framework for handling semi-structured data common in the AEC (Architecture, Engineering, and Construction) industry. By utilizing specialized readers for Primavera P6, IFC-XML, COBie-XML, and buildingSMART Data Dictionary (BSDD) exports, it bridges the gap between legacy XML formats and modern data science workflows. Incorporating Openclaw Skills into your development stack enables you to automate the ingestion of construction schedules and building models directly into analytical environments.
Whether you are dealing with nested namespaces or complex hierarchical structures, the XML Reader simplifies the extraction of activities, resources, and entity types. It transforms raw XML into flat, analysis-ready DataFrames, making it an essential component for any professional building automated construction data pipelines.
XML Reader for Construction Data Use Cases
- Importing Primavera P6 XML exports to analyze project schedules and resource allocations within a data-science context.
- Converting COBie XML data into structured tables for facility management and digital twin building handovers.
- Analyzing IFC-XML building models to count entity types or extract specific architectural properties for BIM auditing.
- Parsing buildingSMART Data Dictionary (BSDD) exports to manage classification systems and property definitions.
- Automating the transformation of semi-structured construction data for integration into larger Openclaw Skills based automation workflows.
How XML Reader for Construction Data Works
- The skill accepts an XML file path or a raw XML string from a construction management system as input.
- It automatically identifies and extracts XML namespaces to ensure accurate element selection and parsing.
- Specialized reader classes for P6, IFC, and COBie target industry-specific tags such as Activity, IfcEntity, or Space.
- Target elements are recursively converted into Python dictionaries, capturing both attributes and nested text values.
- The resulting data is flattened into a pandas DataFrame, providing a clean, tabular structure for analysis.
- This streamlined lifecycle ensures that Openclaw Skills can process complex construction data with minimal manual overhead.
XML Reader for Construction Data Setup
This skill requires Python 3 and the pandas library to handle the data transformation. Ensure your environment is configured by running the following command:
pip install pandas
Once installed, you can integrate the XML Reader logic into your project to start leveraging Openclaw Skills for construction data analysis.
XML Reader for Construction Data Data Schema & Taxonomy
The skill organizes parsed XML data into structured DataFrames using a consistent metadata taxonomy. This schema allows for easy querying and filtering of construction elements:
| Field | Type | Description |
|---|---|---|
_tag |
String | The original XML element tag name (e.g., Activity, IfcWall, or Floor). |
_text |
String | The direct text content extracted from the XML element. |
[attribute] |
String | Original XML attributes are converted to columns named after the attribute. |
[child_tag] |
Mixed | Direct child element values are flattened into the parent row for easy access. |
[child_tag]_[attr] |
Mixed | Attributes of child elements are captured using a prefixed naming convention. |
XML Reader for Construction Data Advanced Features
- Multi-format support tailored for construction-specific schemas including Primavera P6, COBie, IFC-XML, and BSDD.
- Intelligent namespace handling that prevents data loss when parsing complex, standardized XML schemas.
- High-performance entity counting for IFC-XML models, allowing users to rapidly audit model contents.
- Recursive flattening of hierarchical XML structures into flat DataFrames suitable for SQL export or machine learning.
- Designed for extensibility within the Openclaw Skills ecosystem, supporting multi-agent data processing tasks.
SKILL.md
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