Cwicr Data Loader
Load and parse DDC CWICR construction cost database from multiple formats: Parquet, Excel, CSV, Qdrant snapshots. Foundation for all CWICR operations.
Why use this skill?
Efficiently load and parse DDC CWICR construction data from Parquet, Excel, CSV, and Qdrant. Automate cost estimation and schema validation.
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/datadrivenconstruction/cwicr-data-loaderWhat This Skill Does
The Cwicr Data Loader is the foundational engine for all Data Driven Construction (DDC) workflows within the OpenClaw ecosystem. It is designed to ingest, normalize, and validate construction cost data derived from the CWICR database. By abstracting the complexities of file format handling—whether you are dealing with Apache Parquet for high-speed analytics, Excel workbooks for site reporting, CSV files for legacy interoperability, or Qdrant snapshots for vector-based semantic search—this skill provides a unified, type-safe interface for construction professionals and engineers. It automatically detects schemas, ensures type consistency, and converts disparate sources into structured pandas DataFrames, allowing you to focus on cost estimation and project planning rather than data cleaning.
Installation
To integrate this skill into your environment, use the OpenClaw command-line interface. Ensure you have the necessary system dependencies installed first:
pip install pandas pyarrow openpyxl qdrant-client
clawhub install openclaw/skills/skills/datadrivenconstruction/cwicr-data-loader
Use Cases
- Automated Cost Estimation: Load current material and labor rates directly from official DDC Excel sheets to calculate project bids.
- Semantic Construction Querying: Load Qdrant snapshots to enable natural language searching of work item descriptions, allowing agents to find specific construction tasks by context rather than just keyword matches.
- Data Migration & Transformation: Convert legacy CSV or Excel construction data into high-performance Parquet format for long-term project archiving and historical cost trend analysis.
- Multi-Region Analysis: Use the language and region metadata to filter and aggregate construction costs across different global markets, ensuring project budgets account for local labor norms and currency variations.
Example Prompts
- "Load the latest 'Q3_Construction_Rates.xlsx' from the data folder and calculate the total material cost for all masonry work items."
- "Search the CWICR Qdrant database for any work items related to 'reinforced concrete foundations' that have a labor norm under 5 hours."
- "Convert 'legacy_data.csv' into a standardized Parquet format to ensure all unit prices are properly validated against the CWICR schema."
Tips & Limitations
- Memory Management: For massive datasets, leverage the lazy loading capabilities. Avoid loading entire multi-gigabyte Parquet files into memory if you only need a subset of the data.
- Schema Enforcement: Always ensure your source files adhere to the expected columns (e.g., 'work_item_code', 'unit_price'). The loader provides automatic validation, but significant schema deviations will trigger a warning or error to prevent data corruption.
- Currency Awareness: While the loader handles multiple currencies, ensure that your analytical models account for exchange rates if you are aggregating costs from different international regions.
Metadata
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Find the right skillPaste this into your clawhub.json to enable this plugin.
{
"plugins": {
"official-datadrivenconstruction-cwicr-data-loader": {
"enabled": true,
"auto_update": true
}
}
}Tags(AI)
Flags: file-read
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