mcpbeat Sign in

Bim Validation Report Agent Skill

Generate comprehensive BIM model validation reports. Check data quality, completeness, and compliance with standards.

4k 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 bim-validation-report

The instruction itself

12 sections, as written by the author

BIM Validation Report Generator

Business Case

Problem Statement

BIM models often have quality issues:

  • Missing required properties
  • Invalid or inconsistent data
  • Non-compliant with project standards
  • Incomplete model information

Solution

Automated BIM validation system that checks models against configurable rules and generates detailed compliance reports.

Business Value

  • Quality assurance - Catch issues early
  • Standards compliance - Meet project requirements
  • Automation - Reduce manual QC effort
  • Transparency - Clear validation results

Technical Implementation

import pandas as pd
from datetime import datetime
from typing import Dict, Any, List, Optional, Callable
from dataclasses import dataclass, field
from enum import Enum


class ValidationSeverity(Enum):
    """Validation issue severity."""
    ERROR = "error"
    WARNING = "warning"
    INFO = "info"


class ValidationStatus(Enum):
    """Overall validation status."""
    PASSED = "passed"
    PASSED_WITH_WARNINGS = "passed_with_warnings"
    FAILED = "failed"


class RuleCategory(Enum):
    """Validation rule categories."""
    REQUIRED_PROPERTIES = "required_properties"
    DATA_FORMAT = "data_format"
    NAMING_CONVENTION = "naming_convention"
    GEOMETRIC = "geometric"
    CLASSIFICATION = "classification"
    RELATIONSHIPS = "relationships"


@dataclass
class ValidationRule:
    """Single validation rule."""
    rule_id: str
    name: str
    category: RuleCategory
    description: str
    severity: ValidationSeverity
    check_function: Callable
    applicable_categories: List[str] = field(default_factory=list)
    enabled: bool = True


@dataclass
class ValidationIssue:
    """Single validation issue."""
    issue_id: str
    rule_id: str
    rule_name: str
    element_id: str
    element_name: str
    element_category: str
    severity: ValidationSeverity
    message: str
    details: Dict[str, Any] = field(default_factory=dict)

    def to_dict(self) -> Dict[str, Any]:
        return {
            'issue_id': self.issue_id,
            'rule_id': self.rule_id,
            'rule_name': self.rule_name,
            'element_id': self.element_id,
            'element_name': self.element_name,
            'element_category': self.element_category,
            'severity': self.severity.value,
            'message': self.message
        }


@dataclass
class ValidationReport:
    """Complete validation report."""
    project_name: str
    model_name: str
    validated_at: datetime
    status: ValidationStatus
    total_elements: int
    elements_with_issues: int
    issues: List[ValidationIssue]
    rules_checked: int
    summary_by_severity: Dict[str, int]
    summary_by_category: Dict[str, int]


class BIMValidationEngine:
    """BIM model validation engine."""

    def __init__(self, project_name: str, model_name: str):
        self.project_name = project_name
        self.model_name = model_name
        self.rules: List[ValidationRule] = []
        self.issues: List[ValidationIssue] = []
        self._issue_counter = 0

        # Load default rules
        self._load_default_rules()

    def _load_default_rules(self):
        """Load standard validation rules."""

        # Required properties rules
        self.add_rule(ValidationRule(
            rule_id="REQ-001",
            name="Element Name Required",
            category=RuleCategory.REQUIRED_PROPERTIES,
            description="All elements must have a name",
            severity=ValidationSeverity.ERROR,
            check_function=lambda e: bool(e.get('name'))
        ))

        self.add_rule(ValidationRule(
            rule_id="REQ-002",
            name="Level Assignment Required",
            category=RuleCategory.REQUIRED_PROPERTIES,
            description="Elements must be assigned to a level",
            severity=ValidationSeverity.WARNING,
            check_function=lambda e: bool(e.get('level')),
            applicable_categories=["Walls", "Floors", "Doors", "Windows"]
        ))

        self.add_rule(ValidationRule(
            rule_id="REQ-003",
            name="Material Required",
            category=RuleCategory.REQUIRED_PROPERTIES,
            description="Structural elements must have material defined",
            severity=ValidationSeverity.ERROR,
            check_function=lambda e: bool(e.get('material')),
            applicable_categories=["Structural Columns", "Structural Framing", "Floors"]
        ))

        # Naming convention rules
        self.add_rule(ValidationRule(
            rule_id="NAM-001",
            name="No Special Characters",
            category=RuleCategory.NAMING_CONVENTION,
            description="Names should not contain special characters",
            severity=ValidationSeverity.WARNING,
            check_function=self._check_no_special_chars
        ))

        self.add_rule(ValidationRule(
            rule_id="NAM-002",
            name="Name Length Check",
            category=RuleCategory.NAMING_CONVENTION,
            description="Names should be between 3 and 100 characters",
            severity=ValidationSeverity.INFO,
            check_function=lambda e: 3 <= len(e.get('name', '')) <= 100
        ))

        # Classification rules
        self.add_rule(ValidationRule(
            rule_id="CLS-001",
            name="Classification Code Present",
            category=RuleCategory.CLASSIFICATION,
            description="Elements should have classification code",
            severity=ValidationSeverity.WARNING,
            check_function=lambda e: bool(e.get('classification_code') or e.get('uniformat'))
        ))

        # Geometric rules
        self.add_rule(ValidationRule(
            rule_id="GEO-001",
            name="Non-Zero Volume",
            category=RuleCategory.GEOMETRIC,
            description="3D elements must have non-zero volume",
            severity=ValidationSeverity.ERROR,
            check_function=lambda e: float(e.get('volume', 0)) > 0,
            applicable_categories=["Walls", "Floors", "Structural Columns", "Structural Framing"]
        ))

        self.add_rule(ValidationRule(
            rule_id="GEO-002",
            name="Valid Bounding Box",
            category=RuleCategory.GEOMETRIC,
            description="Elements must have valid bounding box",
            severity=ValidationSeverity.ERROR,
            check_function=self._check_valid_bbox
        ))

    def _check_no_special_chars(self, element: Dict[str, Any]) -> bool:
        """Check name for special characters."""
        import re
        name = element.get('name', '')
        return bool(re.match(r'^[\w\s\-\.]+$', name))

    def _check_valid_bbox(self, element: Dict[str, Any]) -> bool:
        """Check for valid bounding box."""
        try:
            min_x = float(element.get('min_x', 0))
            max_x = float(element.get('max_x', 0))
            min_y = float(element.get('min_y', 0))
            max_y = float(element.get('max_y', 0))
            min_z = float(element.get('min_z', 0))
            max_z = float(element.get('max_z', 0))
            return max_x > min_x and max_y > min_y and max_z > min_z
        except (ValueError, TypeError):
            return False

    def add_rule(self, rule: ValidationRule):
        """Add validation rule."""
        self.rules.append(rule)

    def add_custom_rule(self, rule_id: str, name: str, category: RuleCategory,
                       check_function: Callable, severity: ValidationSeverity = ValidationSeverity.WARNING,
                       description: str = "", categories: List[str] = None):
        """Add custom validation rule."""
        rule = ValidationRule(
            rule_id=rule_id,
            name=name,
            category=category,
            description=description,
            severity=severity,
            check_function=check_function,
            applicable_categories=categories or []
        )
        self.add_rule(rule)

    def validate_element(self, element: Dict[str, Any]) -> List[ValidationIssue]:
        """Validate single element against all rules."""
        issues = []
        element_category = element.get('category', '')

        for rule in self.rules:
            if not rule.enabled:
                continue

            # Check if rule applies to this category
            if rule.applicable_categories and element_category not in rule.applicable_categories:
                continue

            try:
                passed = rule.check_function(element)
                if not passed:
                    self._issue_counter += 1
                    issue = ValidationIssue(
                        issue_id=f"ISS-{self._issue_counter:05d}",
                        rule_id=rule.rule_id,
                        rule_name=rule.name,
                        element_id=str(element.get('element_id', '')),
                        element_name=str(element.get('name', '')),
                        element_category=element_category,
                        severity=rule.severity,
                        message=rule.description
                    )
                    issues.append(issue)
            except Exception as e:
                # Rule check failed
                self._issue_counter += 1
                issue = ValidationIssue(
                    issue_id=f"ISS-{self._issue_counter:05d}",
                    rule_id=rule.rule_id,
                    rule_name=rule.name,
                    element_id=str(element.get('element_id', '')),
                    element_name=str(element.get('name', '')),
                    element_category=element_category,
                    severity=ValidationSeverity.ERROR,
                    message=f"Rule check error: {str(e)}"
                )
                issues.append(issue)

        return issues

    def validate_model(self, elements_df: pd.DataFrame) -> ValidationReport:
        """Validate entire BIM model."""
        self.issues = []
        elements_with_issues = set()

        for _, row in elements_df.iterrows():
            element = row.to_dict()
            element_issues = self.validate_element(element)

            if element_issues:
                elements_with_issues.add(element.get('element_id'))
                self.issues.extend(element_issues)

        # Calculate summaries
        summary_by_severity = {
            'error': sum(1 for i in self.issues if i.severity == ValidationSeverity.ERROR),
            'warning': sum(1 for i in self.issues if i.severity == ValidationSeverity.WARNING),
            'info': sum(1 for i in self.issues if i.severity == ValidationSeverity.INFO)
        }

        summary_by_category = {}
        for issue in self.issues:
            cat = issue.element_category
            summary_by_category[cat] = summary_by_category.get(cat, 0) + 1

        # Determine overall status
        if summary_by_severity['error'] > 0:
            status = ValidationStatus.FAILED
        elif summary_by_severity['warning'] > 0:
            status = ValidationStatus.PASSED_WITH_WARNINGS
        else:
            status = ValidationStatus.PASSED

        return ValidationReport(
            project_name=self.project_name,
            model_name=self.model_name,
            validated_at=datetime.now(),
            status=status,
            total_elements=len(elements_df),
            elements_with_issues=len(elements_with_issues),
            issues=self.issues,
            rules_checked=len([r for r in self.rules if r.enabled]),
            summary_by_severity=summary_by_severity,
            summary_by_category=summary_by_category
        )

    def export_report(self, report: ValidationReport, output_path: str):
        """Export validation report to Excel."""
        with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
            # Summary sheet
            summary_data = {
                'Metric': ['Project', 'Model', 'Validated At', 'Status',
                          'Total Elements', 'Elements with Issues', 'Rules Checked',
                          'Errors', 'Warnings', 'Info'],
                'Value': [report.project_name, report.model_name,
                         report.validated_at.isoformat(), report.status.value,
                         report.total_elements, report.elements_with_issues,
                         report.rules_checked, report.summary_by_severity['error'],
                         report.summary_by_severity['warning'], report.summary_by_severity['info']]
            }
            pd.DataFrame(summary_data).to_excel(writer, sheet_name='Summary', index=False)

            # Issues sheet
            issues_df = pd.DataFrame([i.to_dict() for i in report.issues])
            if not issues_df.empty:
                issues_df.to_excel(writer, sheet_name='Issues', index=False)

            # By Category sheet
            cat_df = pd.DataFrame([
                {'Category': k, 'Issue Count': v}
                for k, v in report.summary_by_category.items()
            ])
            if not cat_df.empty:
                cat_df.to_excel(writer, sheet_name='By Category', index=False)

        return output_path


def generate_validation_report(elements_df: pd.DataFrame,
                               project_name: str,
                               model_name: str,
                               output_path: str = None) -> ValidationReport:
    """Quick function to generate validation report."""
    engine = BIMValidationEngine(project_name, model_name)
    report = engine.validate_model(elements_df)

    if output_path:
        engine.export_report(report, output_path)

    return report

Quick Start

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

# Run validation
report = generate_validation_report(
    elements,
    project_name="Office Tower",
    model_name="Architectural Model v3.2",
    output_path="validation_report.xlsx"
)

print(f"Status: {report.status.value}")
print(f"Errors: {report.summary_by_severity['error']}")
print(f"Warnings: {report.summary_by_severity['warning']}")

Common Use Cases

1. Custom Validation Rules

engine = BIMValidationEngine("Project", "Model")

# Add custom rule
engine.add_custom_rule(
    rule_id="CUSTOM-001",
    name="Fire Rating Required",
    category=RuleCategory.REQUIRED_PROPERTIES,
    check_function=lambda e: bool(e.get('fire_rating')),
    severity=ValidationSeverity.ERROR,
    categories=["Walls", "Doors"]
)

2. Filter Issues

# Get only errors
errors = [i for i in report.issues if i.severity == ValidationSeverity.ERROR]

# Get issues for specific category
wall_issues = [i for i in report.issues if i.element_category == "Walls"]

3. Automated QC Pipeline

report = engine.validate_model(elements)
if report.status == ValidationStatus.FAILED:
    send_notification("BIM validation failed", report.summary_by_severity)

Resources

  • DDC Book: Chapter 4.3 - BIM Validation
  • Reference: ISO 19650, buildingSMART IDS

Other skills for the same job

different authors, same section of the catalogue
XLSX
by anthropics
vendor ×15

Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas

5k tokens scripts
XLSX
by w95
×7

Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like \"the xlsx in my downloads\") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

3k tokens
Raffle Winner Picker
by frostant
×5

Picks random winners from lists, spreadsheets, or Google Sheets for giveaways, raffles, and contests. Ensures fair, unbiased selection with transparency.

949 tokens
Fda Database
by christophacham
×4

Query openFDA API for drugs, devices, adverse events, recalls, regulatory submissions (510k, PMA), substance identification (UNII), for FDA regulatory data analysis and safety research.

32k tokens scripts
Matlab
by christophacham
×4

MATLAB and GNU Octave numerical computing for matrix operations, data analysis, visualization, and scientific computing. Use when writing MATLAB/Octave scripts for linear algebra, signal processing, image processing, differential equations, optimization, statistics, or creating scientific visualizations. Also use when the user needs help with MATLAB syntax, functions, or wants to convert between MATLAB and Python code. Scripts can be executed with MATLAB or the open-source GNU Octave interpreter.

25k tokens
Umap Learn
by ComeOnOliver
×4

UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.

14k tokens
D3 Viz
by chrisvoncsefalvay
×3

Creating interactive data visualisations using d3.js. This skill should be used when creating custom charts, graphs, network diagrams, geographic visualisations, or any complex SVG-based data visualisation that requires fine-grained control over visual elements, transitions, or interactions. Use this for bespoke visualisations beyond standard charting libraries, whether in React, Vue, Svelte, vanilla JavaScript, or any other environment.

20k tokens
Alphafold Database
by christophacham
×3

Access AlphaFold 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology.

7k tokens

How to use it

Copy the folder

Take datadrivenconstruction/bim-validation-report 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.