json-parser

What This Skill Does

The JSON Parser skill is a specialized tool designed to ingest, validate, and restructure complex construction-related data. Construction projects generate massive volumes of heterogeneous data, ranging from BIM (Building Information Modeling) exports to IoT environmental sensor streams. This skill simplifies the process by converting nested, multi-layered JSON payloads into a simplified, tabular format that is immediately compatible with data analysis workflows, spreadsheet exports, or project management dashboards. By providing both parsing and flattening capabilities, it transforms hierarchical API responses—which are often difficult for non-technical stakeholders to interpret—into clean, actionable row-based data structures.

Installation

To integrate this skill into your OpenClaw environment, execute the following command in your terminal: clawhub install openclaw/skills/skills/datadrivenconstruction/json-parser

Use Cases

1. BIM Integration: Extract metadata from large IFC-derived JSON exports to create material take-off lists or cost estimation sheets.
2. IoT Monitoring: Flatten real-time data packets from job site air quality or vibration sensors to track equipment health over time.
3. Vendor API Reporting: Standardize data received from various subcontractors' software platforms, allowing for cross-vendor performance comparisons.
4. Database Migration: Prepare JSON-stored project documentation for ingestion into structured SQL databases.

Example Prompts

1. "Parse the BIM_data.json file in my project folder and convert it to a flat spreadsheet so I can see every component's weight and volume."
2. "Take this JSON sensor log and turn it into a pandas DataFrame so I can calculate the average temperature on the third floor."
3. "Validate this JSON string from the crane supplier; if it's valid, flatten the nested sub-assemblies so I can import the list into our inventory manager."

Tips & Limitations

• Deep Nesting: While the flattening algorithm handles most standard hierarchies, extremely deep or circular JSON structures may require pre-processing or recursive depth limits.
• Schema Consistency: This skill works best when the input JSON maintains a consistent schema. If your API returns highly varied data formats within the same list, individual records may result in sparse dataframes with many null values.
• Performance: For extremely large files (e.g., files over 500MB), ensure your host machine has sufficient RAM, as the to_dataframe conversion creates an in-memory representation of the dataset.