LLM Data Automation Skill for Claude

Prompt: "Write Python code to read an Excel file with construction materials,
filter rows where quantity > 100, and save to CSV."

# Install Ollama from ollama.com
ollama pull mistral

# Run a query
ollama run mistral "Write Pandas code to calculate total cost from quantity * unit_price"

import pandas as pd

# Construction project as DataFrame
# Rows = elements, Columns = attributes
df = pd.DataFrame({
    'element_id': ['W001', 'W002', 'C001'],
    'category': ['Wall', 'Wall', 'Column'],
    'material': ['Concrete', 'Brick', 'Steel'],
    'volume_m3': [45.5, 32.0, 8.2],
    'cost_per_m3': [150, 80, 450]
})

# Calculate total cost
df['total_cost'] = df['volume_m3'] * df['cost_per_m3']
print(df)

"Write code to import Excel file with construction schedule,
parse dates, and create a Pandas DataFrame"

"Filter construction elements where category is 'Structural'
and cost exceeds budget limit of 50000"

"Group construction data by floor level,
calculate total volume and cost for each floor"

"Create summary report with material quantities grouped by category,
export to Excel with formatting"

# Prompt to ChatGPT:
# "Write code to extract tables from PDF and convert to DataFrame"

import pdfplumber
import pandas as pd

def pdf_to_dataframe(pdf_path):
    """Extract tables from PDF file"""
    all_tables = []
    with pdfplumber.open(pdf_path) as pdf:
        for page in pdf.pages:
            tables = page.extract_tables()
            for table in tables:
                if table:
                    df = pd.DataFrame(table[1:], columns=table[0])
                    all_tables.append(df)

    if all_tables:
        return pd.concat(all_tables, ignore_index=True)
    return pd.DataFrame()

# Usage
df = pdf_to_dataframe("construction_spec.pdf")
df.to_excel("extracted_data.xlsx", index=False)

# Prompt: "Analyze BIM elements, group by category, calculate volumes"

import pandas as pd

def analyze_bim_elements(csv_path):
    """Analyze BIM element data from CSV export"""
    df = pd.read_csv(csv_path)

    # Group by category
    summary = df.groupby('Category').agg({
        'Volume': 'sum',
        'Area': 'sum',
        'ElementId': 'count'
    }).rename(columns={'ElementId': 'Count'})

    return summary

# Usage
summary = analyze_bim_elements("revit_export.csv")
print(summary)

# Prompt: "Create cost estimation from quantities and unit prices"

import pandas as pd

def calculate_cost_estimate(quantities_df, prices_df):
    """
    Calculate project cost estimate

    Args:
        quantities_df: DataFrame with columns [item_code, quantity]
        prices_df: DataFrame with columns [item_code, unit_price, unit]

    Returns:
        DataFrame with cost calculations
    """
    # Merge quantities with prices
    result = quantities_df.merge(prices_df, on='item_code', how='left')

    # Calculate costs
    result['total_cost'] = result['quantity'] * result['unit_price']

    # Add summary
    result['cost_percentage'] = (result['total_cost'] /
                                  result['total_cost'].sum() * 100).round(2)

    return result

# Usage
quantities = pd.DataFrame({
    'item_code': ['C001', 'S001', 'W001'],
    'quantity': [150, 2000, 500]
})

prices = pd.DataFrame({
    'item_code': ['C001', 'S001', 'W001'],
    'unit_price': [120, 45, 85],
    'unit': ['m3', 'kg', 'm2']
})

estimate = calculate_cost_estimate(quantities, prices)
print(estimate)

# Prompt: "Parse construction schedule, calculate durations, identify delays"

import pandas as pd
from datetime import datetime

def analyze_schedule(schedule_path):
    """Analyze construction schedule for delays"""
    df = pd.read_excel(schedule_path)

    # Parse dates
    df['start_date'] = pd.to_datetime(df['start_date'])
    df['end_date'] = pd.to_datetime(df['end_date'])
    df['actual_end'] = pd.to_datetime(df['actual_end'])

    # Calculate durations
    df['planned_duration'] = (df['end_date'] - df['start_date']).dt.days
    df['actual_duration'] = (df['actual_end'] - df['start_date']).dt.days

    # Identify delays
    df['delay_days'] = df['actual_duration'] - df['planned_duration']
    df['is_delayed'] = df['delay_days'] > 0

    return df

# Usage
schedule = analyze_schedule("project_schedule.xlsx")
delayed_tasks = schedule[schedule['is_delayed']]
print(f"Delayed tasks: {len(delayed_tasks)}")

# Install
curl -fsSL https://ollama.com/install.sh | sh

# Download models
ollama pull mistral      # General purpose, 7B params
ollama pull codellama    # Code-focused
ollama pull deepseek-coder  # Best for coding tasks

# Run
ollama run mistral "Write Pandas code to merge two DataFrames on project_id"

# Load company documents into local LLM
from llama_index import SimpleDirectoryReader, VectorStoreIndex

# Read all PDFs from folder
reader = SimpleDirectoryReader("company_documents/")
documents = reader.load_data()

# Create searchable index
index = VectorStoreIndex.from_documents(documents)

# Query your documents
query_engine = index.as_query_engine()
response = query_engine.query(
    "What are the standard concrete mix specifications?"
)
print(response)

pip install jupyter pandas openpyxl pdfplumber
jupyter notebook