What does the Bim Classification Ai AI skill do?

Classify BIM elements using AI and standard classification systems. Map elements to UniFormat, MasterFormat, OmniClass, and CWICR codes.

Why use Bim Classification Ai on TypingMind?

Because you install it once and use it with any model. Bim Classification Ai is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Bim Classification Ai in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction/tree/main/1_DDC_Toolkit/BIM-Analysis/bim-classification-ai. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Bim Classification Ai?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Bim Classification Ai?

As many as you like. As long as a model supports skills, you can use Bim Classification Ai with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Bim Classification Ai AI skill free?

Yes. It is published on GitHub by datadrivenconstruction under the MIT license. You only pay your own AI provider for the tokens you use.

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

Bim Classification Ai by datadrivenconstruction — AI Skill for Any LLM

BIM Classification AI

Business Case

Problem Statement

BIM models often lack proper classification:

Elements without classification codes
Inconsistent naming conventions
Manual classification is tedious
Difficult to map to cost databases

Solution

AI-powered classification system that analyzes BIM element properties and suggests appropriate classification codes from multiple standards.

Business Value

Automation - Reduce manual classification effort
Consistency - Standardized classification across projects
Integration - Enable cost estimation and QTO
Quality - Improved data quality in BIM models

Technical Implementation

python
import pandas as pd
from typing import Dict, Any, List, Optional, Tuple
from dataclasses import dataclass, field
from enum import Enum
import re


class ClassificationSystem(Enum):
    """Classification standards."""
    UNIFORMAT = "uniformat"
    MASTERFORMAT = "masterformat"
    OMNICLASS = "omniclass"
    UNICLASS = "uniclass"
    CWICR = "cwicr"


@dataclass
class ClassificationCode:
    """Classification code with metadata."""
    code: str
    title: str
    system: ClassificationSystem
    level: int
    parent_code: Optional[str] = None
    keywords: List[str] = field(default_factory=list)


@dataclass
class ClassificationResult:
    """Result of classification attempt."""
    element_id: str
    element_name: str
    element_category: str
    suggested_codes: List[Tuple[ClassificationCode, float]]  # (code, confidence)
    selected_code: Optional[ClassificationCode] = None
    manual_override: bool = False


class ClassificationDatabase:
    """Classification codes database."""

    def __init__(self):
        self.codes: Dict[ClassificationSystem, List[ClassificationCode]] = {
            system: [] for system in ClassificationSystem
        }
        self._load_standard_codes()

    def _load_standard_codes(self):
        """Load standard classification codes."""
        # UniFormat II codes
        uniformat_codes = [
            ("A", "Substructure", 1, None, ["foundation", "basement", "excavation"]),
            ("A10", "Foundations", 2, "A", ["footing", "pile", "foundation"]),
            ("A1010", "Standard Foundations", 3, "A10", ["spread footing", "strip footing"]),
            ("A1020", "Special Foundations", 3, "A10", ["pile", "caisson", "mat foundation"]),
            ("B", "Shell", 1, None, ["superstructure", "exterior", "roof"]),
            ("B10", "Superstructure", 2, "B", ["floor", "roof", "structure"]),
            ("B1010", "Floor Construction", 3, "B10", ["slab", "deck", "floor"]),
            ("B1020", "Roof Construction", 3, "B10", ["roof", "deck", "truss"]),
            ("B20", "Exterior Enclosure", 2, "B", ["wall", "window", "door"]),
            ("B2010", "Exterior Walls", 3, "B20", ["curtain wall", "masonry", "cladding"]),
            ("B2020", "Exterior Windows", 3, "B20", ["window", "glazing", "storefront"]),
            ("B30", "Roofing", 2, "B", ["roof", "membrane", "insulation"]),
            ("C", "Interiors", 1, None, ["partition", "ceiling", "floor finish"]),
            ("C10", "Interior Construction", 2, "C", ["partition", "door", "glazing"]),
            ("C20", "Stairs", 2, "C", ["stair", "railing", "ladder"]),
            ("C30", "Interior Finishes", 2, "C", ["finish", "paint", "flooring"]),
            ("D", "Services", 1, None, ["mechanical", "electrical", "plumbing"]),
            ("D10", "Conveying", 2, "D", ["elevator", "escalator", "lift"]),
            ("D20", "Plumbing", 2, "D", ["pipe", "fixture", "drain"]),
            ("D30", "HVAC", 2, "D", ["duct", "hvac", "air handling"]),
            ("D40", "Fire Protection", 2, "D", ["sprinkler", "fire", "suppression"]),
            ("D50", "Electrical", 2, "D", ["electrical", "power", "lighting"]),
        ]

        for code, title, level, parent, keywords in uniformat_codes:
            self.codes[ClassificationSystem.UNIFORMAT].append(
                ClassificationCode(code, title, ClassificationSystem.UNIFORMAT, level, parent, keywords)
            )

        # MasterFormat codes (simplified)
        masterformat_codes = [
            ("03", "Concrete", 1, None, ["concrete", "formwork", "reinforcing"]),
            ("03 30 00", "Cast-in-Place Concrete", 2, "03", ["concrete", "pour", "slab"]),
            ("03 41 00", "Precast Structural Concrete", 2, "03", ["precast", "concrete", "panel"]),
            ("04", "Masonry", 1, None, ["brick", "block", "stone"]),
            ("05", "Metals", 1, None, ["steel", "metal", "aluminum"]),
            ("05 12 00", "Structural Steel Framing", 2, "05", ["beam", "column", "steel"]),
            ("06", "Wood, Plastics, Composites", 1, None, ["wood", "timber", "lumber"]),
            ("07", "Thermal and Moisture Protection", 1, None, ["insulation", "roofing", "waterproofing"]),
            ("08", "Openings", 1, None, ["door", "window", "glazing"]),
            ("09", "Finishes", 1, None, ["drywall", "paint", "flooring"]),
            ("21", "Fire Suppression", 1, None, ["sprinkler", "fire", "suppression"]),
            ("22", "Plumbing", 1, None, ["pipe", "fixture", "plumbing"]),
            ("23", "HVAC", 1, None, ["hvac", "duct", "mechanical"]),
            ("26", "Electrical", 1, None, ["electrical", "power", "lighting"]),
        ]

        for code, title, level, parent, keywords in masterformat_codes:
            self.codes[ClassificationSystem.MASTERFORMAT].append(
                ClassificationCode(code, title, ClassificationSystem.MASTERFORMAT, level, parent, keywords)
            )

    def search(self, query: str, system: ClassificationSystem = None) -> List[ClassificationCode]:
        """Search classification codes by keyword."""
        results = []
        query_lower = query.lower()

        systems = [system] if system else list(ClassificationSystem)

        for sys in systems:
            for code in self.codes.get(sys, []):
                # Check title
                if query_lower in code.title.lower():
                    results.append(code)
                    continue
                # Check keywords
                if any(query_lower in kw.lower() for kw in code.keywords):
                    results.append(code)

        return results


class BIMClassificationAI:
    """AI-powered BIM element classification."""

    def __init__(self, classification_db: ClassificationDatabase = None):
        self.db = classification_db or ClassificationDatabase()
        self.category_mappings = self._load_category_mappings()
        self.results: List[ClassificationResult] = []

    def _load_category_mappings(self) -> Dict[str, List[str]]:
        """Load Revit/IFC category to classification mappings."""
        return {
            # Structural
            "Structural Columns": ["B10", "05 12 00", "column", "structural"],
            "Structural Framing": ["B10", "05 12 00", "beam", "framing"],
            "Structural Foundations": ["A10", "03 30 00", "foundation", "footing"],
            "Floors": ["B1010", "03 30 00", "floor", "slab"],
            # Architectural
            "Walls": ["B20", "04", "wall", "partition"],
            "Curtain Walls": ["B2010", "08 44 00", "curtain wall", "glazing"],
            "Windows": ["B2020", "08 50 00", "window", "glazing"],
            "Doors": ["C10", "08 10 00", "door", "opening"],
            "Roofs": ["B30", "07 50 00", "roof", "roofing"],
            "Ceilings": ["C30", "09 51 00", "ceiling", "finish"],
            "Stairs": ["C20", "05 51 00", "stair", "railing"],
            # MEP
            "Ducts": ["D30", "23 31 00", "duct", "hvac"],
            "Pipes": ["D20", "22 11 00", "pipe", "plumbing"],
            "Electrical Equipment": ["D50", "26 20 00", "electrical", "panel"],
            "Lighting Fixtures": ["D50", "26 51 00", "light", "fixture"],
            "Sprinklers": ["D40", "21 13 00", "sprinkler", "fire protection"],
            "Mechanical Equipment": ["D30", "23 70 00", "ahu", "hvac equipment"],
        }

    def classify_element(self,
                        element_id: str,
                        element_name: str,
                        category: str,
                        properties: Dict[str, Any] = None,
                        target_systems: List[ClassificationSystem] = None) -> ClassificationResult:
        """Classify a single BIM element."""

        target_systems = target_systems or [ClassificationSystem.UNIFORMAT, ClassificationSystem.MASTERFORMAT]
        suggestions = []

        # Get keywords from category mapping
        keywords = self.category_mappings.get(category, [])

        # Add keywords from element name
        name_words = re.findall(r'\w+', element_name.lower())
        keywords.extend(name_words)

        # Add keywords from properties
        if properties:
            for key, value in properties.items():
                if isinstance(value, str):
                    keywords.extend(re.findall(r'\w+', value.lower()))

        # Search classification codes
        for system in target_systems:
            for keyword in keywords:
                matches = self.db.search(keyword, system)
                for match in matches:
                    confidence = self._calculate_confidence(match, keywords, category)
                    suggestions.append((match, confidence))

        # Remove duplicates and sort by confidence
        seen = set()
        unique_suggestions = []
        for code, conf in sorted(suggestions, key=lambda x: x[1], reverse=True):
            if code.code not in seen:
                seen.add(code.code)
                unique_suggestions.append((code, conf))

        result = ClassificationResult(
            element_id=element_id,
            element_name=element_name,
            element_category=category,
            suggested_codes=unique_suggestions[:5],
            selected_code=unique_suggestions[0][0] if unique_suggestions else None
        )

        self.results.append(result)
        return result

    def _calculate_confidence(self, code: ClassificationCode,
                             keywords: List[str], category: str) -> float:
        """Calculate classification confidence score."""
        score = 0.0

        # Direct category match
        if category in self.category_mappings:
            if code.code in self.category_mappings[category]:
                score += 0.5

        # Keyword matches
        keyword_matches = sum(1 for kw in keywords if kw.lower() in
                            [k.lower() for k in code.keywords])
        score += min(keyword_matches * 0.1, 0.3)

        # Title match
        title_words = code.title.lower().split()
        title_matches = sum(1 for kw in keywords if kw.lower() in title_words)
        score += min(title_matches * 0.1, 0.2)

        return min(score, 1.0)

    def classify_batch(self, elements_df: pd.DataFrame,
                      id_column: str = 'element_id',
                      name_column: str = 'name',
                      category_column: str = 'category') -> pd.DataFrame:
        """Classify multiple elements from DataFrame."""

        results = []
        for _, row in elements_df.iterrows():
            result = self.classify_element(
                element_id=str(row[id_column]),
                element_name=str(row[name_column]),
                category=str(row[category_column]),
                properties=row.to_dict()
            )

            results.append({
                'element_id': result.element_id,
                'element_name': result.element_name,
                'category': result.element_category,
                'uniformat_code': next((c.code for c, _ in result.suggested_codes
                                       if c.system == ClassificationSystem.UNIFORMAT), None),
                'masterformat_code': next((c.code for c, _ in result.suggested_codes
                                          if c.system == ClassificationSystem.MASTERFORMAT), None),
                'confidence': result.suggested_codes[0][1] if result.suggested_codes else 0
            })

        return pd.DataFrame(results)

    def get_summary(self) -> Dict[str, Any]:
        """Get classification summary."""
        total = len(self.results)
        classified = sum(1 for r in self.results if r.selected_code)
        high_confidence = sum(1 for r in self.results
                            if r.suggested_codes and r.suggested_codes[0][1] > 0.7)

        return {
            'total_elements': total,
            'classified': classified,
            'classification_rate': round(classified / total * 100, 1) if total > 0 else 0,
            'high_confidence': high_confidence,
            'high_confidence_rate': round(high_confidence / total * 100, 1) if total > 0 else 0
        }

    def export_results(self) -> pd.DataFrame:
        """Export classification results to DataFrame."""
        data = []
        for result in self.results:
            row = {
                'element_id': result.element_id,
                'element_name': result.element_name,
                'category': result.element_category,
                'selected_code': result.selected_code.code if result.selected_code else None,
                'selected_title': result.selected_code.title if result.selected_code else None,
                'selected_system': result.selected_code.system.value if result.selected_code else None,
                'manual_override': result.manual_override
            }

            # Add top suggestions
            for i, (code, conf) in enumerate(result.suggested_codes[:3]):
                row[f'suggestion_{i+1}_code'] = code.code
                row[f'suggestion_{i+1}_confidence'] = round(conf, 2)

            data.append(row)

        return pd.DataFrame(data)

Quick Start

python
# Initialize classifier
classifier = BIMClassificationAI()

# Classify single element
result = classifier.classify_element(
    element_id="12345",
    element_name="Concrete Floor Slab Level 2",
    category="Floors",
    properties={'material': 'Concrete', 'thickness': '200mm'}
)

print(f"Suggested: {result.selected_code.code} - {result.selected_code.title}")
print(f"Confidence: {result.suggested_codes[0][1]:.1%}")

Common Use Cases

1. Batch Classification

python
# Load BIM elements
elements = pd.read_excel("bim_elements.xlsx")

# Classify all
classified = classifier.classify_batch(elements)
classified.to_excel("classified_elements.xlsx")

2. Map to CWICR

python
# Get UniFormat code for cost mapping
uniformat = result.selected_code.code
cwicr_code = map_uniformat_to_cwicr(uniformat)

3. Quality Check

python
summary = classifier.get_summary()
print(f"Classification rate: {summary['classification_rate']}%")

Resources

DDC Book: Chapter 2.5 - Data Standards
Reference: UniFormat II, CSI MasterFormat

Publisher	datadrivenconstruction
Repository	`DDC_Skills_for_AI_Agents_in_Construction`
Skill name	`bim-classification-ai`
Stars	365
Forks	91
Bundled files	2
License	MIT
Links	Skill source Repository Homepage

Bim Classification Ai

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

BIM Classification AI

Business Case

Problem Statement

Solution

Business Value

Technical Implementation

Quick Start

Common Use Cases

1. Batch Classification

2. Map to CWICR

3. Quality Check

Resources

Bundled files

Frequently asked questions

Set up your own AI workspace now

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

BIM Classification AI

Business Case

Problem Statement

Solution

Business Value

Technical Implementation

Quick Start

Common Use Cases

1. Batch Classification

2. Map to CWICR

3. Quality Check

Resources

Bundled files

Frequently asked questions

Related AI Skills

Set up your own AI workspace now