llm-data-automation

What This Skill Does

The llm-data-automation skill acts as an AI-powered interface for construction data transformation. It bridges the gap between complex construction project data—such as Excel-based material lists, CAD-exported schedules, or procurement logs—and actionable Python analytics. By leveraging the reasoning capabilities of LLMs like ChatGPT, Claude, or local LLaMA models via Ollama, users can perform sophisticated data manipulation without writing code from scratch. The skill follows the Data Driven Construction (DDC) methodology, treating construction elements as uniform Pandas DataFrames, allowing for rapid cleaning, filtering, and aggregation of project metrics.

Installation

To integrate this capability into your OpenClaw environment, execute the following command in your terminal:

clawhub install openclaw/skills/skills/datadrivenconstruction/llm-data-automation

Ensure that you have an environment configured with pandas and either an active API key for cloud-based LLMs or a local runtime like Ollama installed for privacy-focused processing.

Use Cases

• Automating Bill of Quantities (BoQ) verification against project budgets.
• Transforming inconsistent supplier price lists into standardized internal cost databases.
• Cleaning and validating construction site logs for safety and progress reporting.
• Merging disparate data sources such as BIM schedules and cost sheets into a unified DataFrame for cross-reference.
• Rapid generation of monthly project status summaries and material procurement reports.

Example Prompts

1. "Analyze my project_schedule.xlsx file. Identify any activities that have a duration exceeding 10 days and calculate the total resource cost for these long-duration items using the 'cost_per_day' column."
2. "Write a Pandas script to group all construction elements by 'floor_level' and 'material_type', then calculate the sum of 'volume_m3' for each group, and output the result as a formatted Excel summary report."
3. "Filter my inventory list for items where the quantity currently in stock is less than the 'minimum_required' threshold, and suggest a procurement order format based on the 'supplier_name' column."

Tips & Limitations

• Data Privacy: If processing sensitive project budgets or proprietary site data, prioritize the use of local LLMs (e.g., via Ollama or LM Studio) rather than public cloud APIs.
• Verification: Always inspect generated Python scripts before execution. LLMs are powerful but occasionally produce code that requires minor debugging for specific column naming conventions.
• Formatting: Ensure your input data is clean and headers are clearly labeled for the best results when requesting automated scripts.
• Scaling: For extremely large datasets (millions of rows), consider that local LLMs might reach hardware resource limits; partition large files into logical stages.