mcpbeat

Nobim Image Generator

datadrivenconstruction/nobim-image-generator

Generate images and visualizations from Revit/IFC files without BIM software. Python-based noBIM tool for batch processing.

3k tokens
context cost
the whole folder, loaded on every use
3
files
instructions only
0
copies elsewhere
how many repositories repackaged it
264
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill nobim-image-generator

The instruction itself

14 sections, as written by the author

noBIM Image Generator

Business Case

Problem Statement

Creating visualizations from BIM models typically requires:

  • Expensive BIM software licenses
  • Manual screenshot capture
  • Time-consuming rendering
  • Impossible to batch process

Solution

noBIM tool extracts data and generates visualizations using Python libraries, processing hundreds of projects without BIM software.

Business Value

  • No license required - Pure Python solution
  • Batch processing - Generate images for 1000s of projects
  • Customizable - Create exactly the visualizations you need
  • Automatable - Integrate into data pipelines

Technical Implementation

Installation

pip install pandas matplotlib seaborn plotly ifcopenshell

Core Functionality

import pandas as pd
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
from pathlib import Path
from typing import List, Optional, Tuple

class NoBIMVisualizer:
    def __init__(self):
        self.elements = None
        self.project_name = ""

    def load_from_excel(self, xlsx_path: str) -> int:
        """Load BIM data from converted Excel file."""
        self.elements = pd.read_excel(xlsx_path, sheet_name="Elements")
        self.project_name = Path(xlsx_path).stem
        return len(self.elements)

    def generate_3d_scatter(self, output_path: str,
                            color_by: str = "Category",
                            size: Tuple[int, int] = (12, 10)) -> str:
        """Generate 3D scatter plot of elements."""
        if not all(col in self.elements.columns
                   for col in ['BBox_CenterX', 'BBox_CenterY', 'BBox_CenterZ']):
            raise ValueError("Bounding box data required. Export with 'bbox' option.")

        fig = plt.figure(figsize=size)
        ax = fig.add_subplot(111, projection='3d')

        # Get unique categories for coloring
        categories = self.elements[color_by].unique()
        colors = plt.cm.tab20(np.linspace(0, 1, len(categories)))
        color_map = dict(zip(categories, colors))

        for cat in categories:
            subset = self.elements[self.elements[color_by] == cat]
            ax.scatter(
                subset['BBox_CenterX'],
                subset['BBox_CenterY'],
                subset['BBox_CenterZ'],
                c=[color_map[cat]],
                label=cat[:20],
                alpha=0.6,
                s=10
            )

        ax.set_xlabel('X')
        ax.set_ylabel('Y')
        ax.set_zlabel('Z')
        ax.set_title(f'{self.project_name} - 3D Element Distribution')
        ax.legend(loc='upper left', fontsize=8, ncol=2)

        plt.savefig(output_path, dpi=150, bbox_inches='tight')
        plt.close()
        return output_path

    def generate_floor_plan(self, output_path: str, level: str,
                            size: Tuple[int, int] = (14, 10)) -> str:
        """Generate floor plan visualization for specific level."""
        level_elements = self.elements[self.elements['Level'] == level]

        if level_elements.empty:
            raise ValueError(f"No elements found for level: {level}")

        fig, ax = plt.subplots(figsize=size)

        # Draw walls
        walls = level_elements[level_elements['Category'] == 'Walls']
        for _, wall in walls.iterrows():
            rect = plt.Rectangle(
                (wall['BBox_MinX'], wall['BBox_MinY']),
                wall['BBox_MaxX'] - wall['BBox_MinX'],
                wall['BBox_MaxY'] - wall['BBox_MinY'],
                fill=True, facecolor='gray', edgecolor='black', alpha=0.7
            )
            ax.add_patch(rect)

        # Draw rooms
        rooms = level_elements[level_elements['Category'] == 'Rooms']
        for _, room in rooms.iterrows():
            center_x = (room['BBox_MinX'] + room['BBox_MaxX']) / 2
            center_y = (room['BBox_MinY'] + room['BBox_MaxY']) / 2
            ax.annotate(room.get('RoomName', 'Room'),
                       (center_x, center_y), ha='center', fontsize=8)

        ax.set_aspect('equal')
        ax.set_title(f'{self.project_name} - {level}')
        ax.set_xlabel('X (m)')
        ax.set_ylabel('Y (m)')

        plt.savefig(output_path, dpi=150, bbox_inches='tight')
        plt.close()
        return output_path

    def generate_category_chart(self, output_path: str,
                                 size: Tuple[int, int] = (12, 8)) -> str:
        """Generate bar chart of element categories."""
        cat_counts = self.elements['Category'].value_counts().head(20)

        fig, ax = plt.subplots(figsize=size)
        bars = ax.barh(cat_counts.index, cat_counts.values,
                       color=plt.cm.viridis(np.linspace(0, 1, len(cat_counts))))

        ax.set_xlabel('Element Count')
        ax.set_title(f'{self.project_name} - Element Categories')

        # Add count labels
        for bar, count in zip(bars, cat_counts.values):
            ax.text(bar.get_width() + 1, bar.get_y() + bar.get_height()/2,
                   f'{count}', va='center', fontsize=9)

        plt.tight_layout()
        plt.savefig(output_path, dpi=150, bbox_inches='tight')
        plt.close()
        return output_path

    def generate_volume_treemap(self, output_path: str) -> str:
        """Generate treemap of volumes by category."""
        import plotly.express as px

        vol_by_cat = self.elements.groupby('Category')['Volume'].sum().reset_index()
        vol_by_cat = vol_by_cat[vol_by_cat['Volume'] > 0].sort_values('Volume', ascending=False)

        fig = px.treemap(
            vol_by_cat.head(30),
            path=['Category'],
            values='Volume',
            title=f'{self.project_name} - Volume Distribution'
        )

        fig.write_image(output_path)
        return output_path

    def batch_generate(self, xlsx_files: List[str], output_dir: str) -> List[str]:
        """Generate standard visualizations for multiple projects."""
        output_dir = Path(output_dir)
        output_dir.mkdir(parents=True, exist_ok=True)

        generated = []
        for xlsx in xlsx_files:
            try:
                self.load_from_excel(xlsx)
                base_name = Path(xlsx).stem

                # Generate all visualizations
                self.generate_3d_scatter(str(output_dir / f"{base_name}_3d.png"))
                self.generate_category_chart(str(output_dir / f"{base_name}_categories.png"))

                generated.append(base_name)
                print(f"Generated visualizations for: {base_name}")

            except Exception as e:
                print(f"Error processing {xlsx}: {e}")

        return generated

Usage Examples

Single Project

viz = NoBIMVisualizer()
viz.load_from_excel("C:/Projects/Office.xlsx")

# Generate 3D view
viz.generate_3d_scatter("office_3d.png", color_by="Category")

# Generate floor plan
viz.generate_floor_plan("office_level1.png", level="Level 1")

# Generate category breakdown
viz.generate_category_chart("office_categories.png")

Batch Processing

from pathlib import Path

viz = NoBIMVisualizer()

# Find all converted files
xlsx_files = list(Path("C:/ConvertedProjects").glob("*.xlsx"))

# Generate visualizations for all
generated = viz.batch_generate(
    [str(f) for f in xlsx_files],
    output_dir="C:/Visualizations"
)

print(f"Generated visualizations for {len(generated)} projects")

Output Examples

| Visualization | Use Case |

|---------------|----------|

| 3D Scatter | Overall project structure |

| Floor Plan | Level-by-level layout |

| Category Chart | Element distribution |

| Volume Treemap | Material quantities |

| Level Comparison | Multi-floor analysis |

Integration with Reporting

from reportlab.pdfgen import canvas
from reportlab.lib.pagesizes import A4

def create_project_report(xlsx_path: str, output_pdf: str):
    """Generate PDF report with all visualizations."""
    viz = NoBIMVisualizer()
    viz.load_from_excel(xlsx_path)

    # Generate images
    images = {
        '3D View': viz.generate_3d_scatter("temp_3d.png"),
        'Categories': viz.generate_category_chart("temp_cat.png"),
    }

    # Create PDF
    c = canvas.Canvas(output_pdf, pagesize=A4)
    c.drawString(100, 800, f"Project Report: {viz.project_name}")

    y_pos = 700
    for title, img_path in images.items():
        c.drawString(100, y_pos, title)
        c.drawImage(img_path, 100, y_pos - 300, width=400, height=280)
        y_pos -= 350

    c.save()
    return output_pdf

Resources

How to use it

Copy the folder

Take datadrivenconstruction/nobim-image-generator from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.

Install what it needs

The instructions reference pip. Without those the skill loads but fails at the first command.